Measurement method and system

ABSTRACT

Methods and systems for determining an individual gaze value are disclosed herein. An exemplary method involves: (a) receiving gaze data for a first wearable computing device, wherein the gaze data is indicative of a wearer-view associated with the first wearable computing device, and wherein the first wearable computing device is associated with a first user-account; (b) analyzing the gaze data from the first wearable computing device to detect one or more occurrences of one or more advertisement spaces in the gaze data; (c) based at least in part on the one or more detected advertisement-space occurrences, determining an individual gaze value for the first user-account; and (d) sending a gaze-value indication, wherein the gaze-value indication indicates the individual gaze value for the first user-account.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 15/257,732, filed Sep. 6, 2016, which is a continuation of U.S.patent application Ser. No. 14/524,753, filed Oct. 27, 2014, which is acontinuation of U.S. patent application Ser. No. 13/292,909, filed Nov.9, 2011, all of which are incorporated by reference herein in theirentirety for all purposes; and is also a continuation-in-part of U.S.patent application Ser. No. 15/145,125, filed May 3, 2016, which is acontinuation of U.S. patent application Ser. No. 13/292,898, filed Nov.9, 2011, both of which are incorporated by reference herein in theirentirety for all purposes.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

Computing devices such as personal computers, laptop computers, tabletcomputers, cellular phones, and countless types of Internet-capabledevices are increasingly prevalent in numerous aspects of modern life.Over time, the manner in which these devices are providing informationto users is becoming more intelligent, more efficient, more intuitive,and/or less obtrusive.

The trend toward miniaturization of computing hardware, peripherals, aswell as of sensors, detectors, and image and audio processors, amongother technologies, has helped open up a field sometimes referred to as“wearable computing.” In the area of image and visual processing andproduction, in particular, it has become possible to consider wearabledisplays that place a very small image display element close enough to awearer's (or user's) eye(s) such that the displayed image fills ornearly fills the field of view, and appears as a normal sized image,such as might be displayed on a traditional image display device. Therelevant technology may be referred to as “near-eye displays.”

Near-eye displays are fundamental components of wearable displays, alsosometimes called “head-mounted displays” (HMDs). A head-mounted displayplaces a graphic display or displays close to one or both eyes of awearer. To generate the images on a display, a computer processingsystem may be used. Such displays may occupy a wearer's entire field ofview, or only occupy part of wearer's field of view. Further,head-mounted displays may be as small as a pair of glasses or as largeas a helmet.

Emerging and anticipated uses of wearable displays include applicationsin which users interact in real time with an augmented or virtualreality. Such applications can be mission-critical or safety-critical,such as in a public safety or aviation setting. The applications canalso be recreational, such as interactive gaming.

SUMMARY

In one aspect, an exemplary computer-implemented method may involve: (a)receiving gaze data for a first wearable computing device, wherein thegaze data is indicative of a wearer-view associated with the firstwearable computing device, and wherein the first wearable computingdevice is associated with a first user-account; (b) analyzing the gazedata from the first wearable computing device to detect one or moreoccurrences of one or more advertisement spaces in the gaze data; (c)based at least in part on the one or more detected advertisement-spaceoccurrences, determining an individual gaze value for the firstuser-account; and (d) sending a gaze-value indication, wherein thegaze-value indication indicates the individual gaze value for the firstuser-account.

In another aspect, an exemplary system may include a non-transitorycomputer-readable medium and program instructions stored on thenon-transitory computer-readable medium. The program instructions may beexecutable by at least one processor to: (a) receive gaze data for afirst wearable computing device, wherein the gaze data is indicative ofa wearer-view associated with the first wearable computing device, andwherein the first wearable computing device is associated with a firstuser-account; (b) analyze the gaze data from the first wearablecomputing device to detect one or more occurrences of one or moreadvertisement spaces in the gaze data; (c) based at least in part on theone or more detected advertisement-space occurrences, determine anindividual gaze value for the first user-account; and (d) send agaze-value indication, wherein the gaze-value indication indicates theindividual gaze value for the first user-account.

In yet another aspect, an exemplary article of manufacture may include acomputer-readable storage medium having instructions stored thereonthat, in response to execution by a processor, cause the processor toperform operations. The instructions may include: (a) instructions forreceiving gaze data for a first wearable computing device, wherein thegaze data is indicative of a wearer-view associated with the firstwearable computing device, and wherein the first wearable computingdevice is associated with a first user-account; (b) instructions foranalyzing the gaze data from the first wearable computing device todetect one or more occurrences of one or more advertisement spaces inthe gaze data; (c) instructions for based at least in part on the one ormore detected advertisement-space occurrences, determining an individualgaze value for the first user-account; and (d) instructions for sendinga gaze-value indication, wherein the gaze-value indication indicates theindividual gaze value for the first user-account.

In a further aspect, an exemplary computer-implemented method mayinvolve: (a) receiving gaze data for a first wearable computing device,wherein the gaze data is indicative of a wearer-view associated with thefirst wearable computing device, and wherein the first wearablecomputing device is associated with a first user-account; (b) analyzingthe gaze data from the first wearable computing device to detect one ormore occurrences of one or more advertisements in the gaze data; (c)based at least in part on the one or more detected advertisementoccurrences, determining an individual gaze value for the firstuser-account; and (d) sending a gaze-value indication to the firstuser-account, wherein the gaze-value indication indicates the individualgaze value for the first user-account.

In yet a further aspect, an exemplary computer-implemented method mayinvolve: (a) receiving, at a wearable computing device, gaze data thatis indicative of a wearer-view associated with the wearable computingdevice; (b) the wearable computing device analyzing the gaze data todetect one or more occurrences of one or more advertisement spaces inthe gaze data; (c) based at least in part on the one or more detectedadvertisement-space occurrences, the wearable computing devicedetermining an individual gaze value for a user-account that isassociated with the wearable computing device; and (d) the wearablecomputing device displaying the individual gaze value.

In an additional aspect, an exemplary system may include anon-transitory computer-readable medium and program instructions storedon the non-transitory computer-readable medium. The program instructionsmay be executable by at least one processor to: (a) receive gaze datathat is indicative of a wearer-view associated with the wearablecomputing device; (b) analyze the gaze data to detect one or moreoccurrences of one or more advertisement spaces in the gaze data; (c)based at least in part on the one or more detected advertisement-spaceoccurrences, determine an individual gaze value for a user-account thatis associated with the wearable computing device; and (d) display theindividual gaze value.

These as well as other aspects, advantages, and alternatives, willbecome apparent to those of ordinary skill in the art by reading thefollowing detailed description, with reference where appropriate to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart illustrating a method according to an exemplaryembodiment.

FIG. 2 is a simplified block diagram illustrating a communicationnetwork via which gaze data may be collected, according to an exemplaryembodiment.

FIG. 3A is a flow chart illustrating a method for determining gazevalue, according to an exemplary embodiment.

FIG. 3B is a flow chart illustrating a method for using multiple factorsto determine an individual gaze value for a user-account, according toan exemplary embodiment.

FIG. 4 is a flow chart illustrating a method for determining gaze value,according to an exemplary embodiment.

FIG. 5A is a flow chart illustrating a method for determiningadvertisement value, according to an exemplary embodiment.

FIG. 5B is a flow chart illustrating a method for determining ad value,according to an exemplary embodiment.

FIG. 6 is a flow chart illustrating a method that may be carried out ata wearable computing device, according to an exemplary embodiment.

FIG. 7A illustrates a wearable computing system according to anexemplary embodiment.

FIG. 7B illustrates an alternate view of the wearable computing deviceillustrated in FIG. 7A.

FIG. 8A illustrates another wearable computing system according to anexemplary embodiment.

FIG. 8B illustrates another wearable computing system according to anexemplary embodiment.

FIG. 9 illustrates a schematic drawing of a wearable computing deviceaccording to an exemplary embodiment.

FIG. 10 is another flow chart illustrating a method according to anexemplary embodiment.

FIG. 11A is a flow chart illustrating a method for updating a variablerate for advertisement rights, according to an exemplary embodiment.

FIG. 11B is a flow chart illustrating another method for updating avariable rate for advertisement rights, according to an exemplaryembodiment.

FIG. 12 is a flow chart illustrating an auction process foradvertisement rights, according to an exemplary embodiment.

FIG. 13 is a flow chart illustrating a method for providing anadvertisement space valuation in an advertisement marketplace, accordingto an exemplary embodiment.

FIG. 14 is flow chart illustrating a method for locating potentialadvertisement spaces, according to an exemplary embodiment.

FIG. 15 is flow chart illustrating a method for providing asearch-request feature, according to an exemplary embodiment.

FIG. 16 is flow chart illustrating a method that providesadvertiser-request functionality, according to an exemplary embodiment.

FIG. 17A is a flow chart illustrating a method for determiningadvertisement value according to an exemplary embodiment.

FIG. 17B is a flow chart illustrating another method for determiningadvertisement value, according to an exemplary embodiment.

FIG. 18 is a flow chart illustrating a method for using multiple factorsto determine the ad-value contribution of a given ad-space occurrence ingaze data, according to an exemplary embodiment.

DETAILED DESCRIPTION

Exemplary methods and systems are described herein. It should beunderstood that the word “exemplary” is used herein to mean “serving asan example, instance, or illustration.” Any embodiment or featuredescribed herein as “exemplary” is not necessarily to be construed aspreferred or advantageous over other embodiments or features. Theexemplary embodiments described herein are not meant to be limiting. Itwill be readily understood that certain aspects of the disclosed systemsand methods can be arranged and combined in a wide variety of differentconfigurations, all of which are contemplated herein.

I. Overview

A. Valuing Ad Space Based on Gaze Data

Many existing methodologies for valuing physical advertising spaceinvolve use of different types of data to estimate how many people viewan advertisement space (referred to interchangeably as an “ad space”)and/or how effective the advertisement space is at delivering theintended message to the viewer. These methodologies often rely ondemographic information and other such indirect measurements of thepotential audience for an ad space. Since these methodologies onlyestimate how many people actually view an ad space and/or who the peopleare that actually view the ad space, and do not incorporate actualviewership data, the results are often inaccurate.

Some existing valuation techniques do incorporate actual viewershipdata, which is typically collected for a test group and thenextrapolated to the population as a whole (or to a larger group, such asa target market). However, gathering such viewership data with existingmethodologies can often be time-consuming and difficult. For example,such techniques often involve polling people in a test groupindividually or laboriously observing how many people actually view anad space (e.g., counting vehicles that pass by a billboard). Because ofthe effort required, advertising is typically limited to certain definedtypes of spaces (e.g., billboards, television commercials, websites,etc.) for which representative viewership data can be most-readilyobtained.

Since most any physical space that is seen by people has some value forpurposes of advertising, current valuation techniques do not allow forcapitalization of many would-be advertising spaces. While the individualvalues of such spaces may be small, the cumulative value of all suchspaces may be significant. However, due to the limitations of currentadvertisement valuation and marketing techniques, much of the potentialvalue of such spaces has not been monetized.

Accordingly, exemplary methods and systems may help to determine thevalue of advertising spaces in a manner that may be more accurate, andmay require less data-collection effort than existingadvertisement-valuation techniques. In particular, exemplary methods mayutilize “gaze data,” from a number of wearable computers, which isindicative of what the wearers of the wearable computers are actuallyviewing, in order to value physical spaces.

For example, point-of-view (POV) videos from a number of wearablecomputers may be analyzed in order to determine how frequently a certainspace is captured in the POV videos. Notably, POV video from wearablecomputers may provide a fairly accurate indication of what a user isactually looking at. Therefore, aggregating such POV videos from anumber of users may help to more accurately value advertising rights tophysical spaces. Additionally, the wearers of the wearable computingdevices may elect to make their respective user-profiles available, suchthat individual characteristics of each wearer who views anadvertisement space may be considered. This information may then be usedto determine the value of the physical space.

Furthermore, a cloud-based server system may aggregate gaze data frommany wearable computers, and use the gaze data to determine wearer-viewdata for various advertisement spaces. As such, an exemplary embodimentmay provide advertisement valuation that is carried out automatically,without the effort required for manual data collection, and is moreaccurate, as the valuation is based on data that more-accuratelycaptures what people are actually viewing.

B. Gaze Valuation for Individual Users

In a further aspect, an exemplary method may be implemented to determinewhat an individual user's gaze is worth. In particular, a user who iswearing a wearable computer with a head-mounted display (HMD) may sendPOV video from a camera attached to their HMD to an advertisement serversystem. Further, the user may opt in to a program where the POV video(and possibly other forms of gaze data) can be used for gaze valuationand/or to value ad spaces. Accordingly, the server system may analyzethe gaze data to detect when the user views ad spaces. The individualgaze value for the user can then be determined based on the ad spacesthat the user has viewed.

Further, in many instances, additional information, such as consumerdata, demographic information, income, job title, hobbies, and/orinterests, among others, may also be considered when determining a gazevalue. For example, consider two users who view the exact sameadvertisements. In this scenario, the gaze value for one of these peoplemight still be higher than for the other if, for example, one person hasa significantly higher income than the other. Other examples are alsopossible.

Yet further, the historical efficacy of advertisements may be consideredwhen determining a gaze value. For example, consider again two peoplewho view the exact same advertisements. If, in the past, one user seemsto have been influenced more by advertisements (e.g., as indicated by apattern of purchasing products subsequent to viewing advertisements forthe products), then this user's gaze value may be higher. Other examplesare also possible.

To obtain their gaze value, a user may create a user-account andregister a wearable computing device (and possibly other devices) totheir user-account. As such, when the server receives gaze data from agiven device, the server may associate the gaze data with theuser-account to which the given device is registered. Then, each timethe server detects an advertisement space in gaze data associated with agiven user-account, the server may determine what the occurrence of thead space is worth (e.g., what having the user view the ad space is worthto an advertiser). This value may be referred to as the “gaze-valuecontribution” for the occurrence of the ad space in the gaze data. Assuch, the server may determine the user's individual gaze value based ongaze-value contributions from a number of ad spaces that occur in theuser's gaze data.

As a specific example, the individual gaze value may be calculated as adollar amount per day. As such, the server may monitor a given user'sgaze data for occurrences of ad spaces, determine a gaze-valuecontribution for each occurrence of an ad space that is detected duringa one-day period, and then determine an individual gaze value by summinggaze-value contributions from the one-day period. Alternatively, theserver may sum the gaze-value contributions for ad-space occurrences ona daily basis, and determine the individual gaze value by averaging thedaily total over a number of days. Other variations are of coursepossible.

Providing users with individual gaze values may be useful in variousscenarios. In one aspect, the ability to learn an individual gaze valuemay be used as an incentive for a user to opt in to a program where theuser provides and authorizes use of their gaze data. In particular, whengaze data is used to value advertisement spaces, increasing the numberof wearable computing devices from which gaze data is available willtypically increase the number of ad spaces that can be valued and/orimprove how accurately these ad spaces are valued. Accordingly, a usermay be provided with access to individual gaze valuation functions onlyafter the user has created a user-account, agreed to provide gaze datafrom their wearable computing device (and possibly from other devicesthat the user may optionally associate with their user account), andauthorized their gaze data to be used for purposes of advertisementvaluation. Since knowing what their individual gaze is worth may beinteresting to many users, access to this functionality may be anincentive for such users to provide and authorize use of their gazedata.

Furthermore, in some embodiments, individual gaze value may be more thanjust a metric that interests users. Rather, individual gaze valuefunctionality may be utilized to determine payments that are actuallypaid out users who provide gaze data. More specifically, since gaze datais typically indicative of what a user is actually viewing, an ad spacemarketplace may be established where advertisers pay for rights to adspaces that are valued using gaze data, and a portion of the money paidby the advertisers is distributed to the users who provide the gazedata. In particular, when an occurrence of an ad space is detected ingaze data for a given user-account, the server system may updatewearer-view data used for valuation of the ad space, and may alsodetermine a value to the advertiser of the particular user viewing thead space (e.g., a gaze-value contribution for the occurrence of the adspace in the user's gaze data). A portion of this value may then becredited to the user-account and/or paid out to the user.

As an example of one such application, an ad marketplace may be set upto pay users who provide gaze data a 5% commission on ad spaces theyview. As such, users who allow their gaze data to be used for advaluation may be paid 5% of their individual gaze value in exchange foruse of their gaze data. Other examples are also possible.

Note that herein, when gaze data is said to be associated with a givenuser-account, it should generally be understood that this gaze data wassent by a device that is associated with the given user-account (e.g., adevice that is registered with the user-account). Further, gaze dataand/or other information that is associated with a user-account may alsobe said to be associated with a user since, functionally, associatinggaze data or any other data with a user will generally be accomplishedby associating the data with the user's user account.

In a further aspect, when a user creates a user-account for which a gazevalue may be determined, a user-profile for the user-account may becreated as well. The user-profile may include or provide access tovarious types of information, from various sources, which is related tothe user. For simplicity, examples set forth herein may simply refer toa user-account as including the information included in the associateduser-profile. However, this should not be read as requiring that auser-account include a user-profile. It is possible, in someembodiments, that a user-account may not have an associateduser-profile.

C. Display on Non-Traditional Surfaces

In a further aspect, exemplary methods and systems may automaticallycollect wearer-view data for a large number of advertisement spaces.This may allow for valuation and monetization of many physical spacesthat have value for purposes of advertising, and might not otherwise becapitalized.

For example, the shortcomings of existing advertisement valuation areespecially problematic when it comes to individuals who wish toassociate themselves with a brand or product (e.g., individuals who wishto be sponsored). Because of the cost and/or effort involved in usingcommon valuation techniques to gather viewership data for the averageperson, sponsorship is generally limited to high-visibility individuals(e.g., famous athletes and musicians). Such high-visibility individualsare typically the most valuable to an advertiser, such that the cost ofevaluating the sponsorship is justified by the value to the advertiser.For instance, a famous race-car driver can readily sell advertisingspace on his or her clothing or on their car. However, there is nomarket for the average person to do the same (in fact, individuals oftenpay more to have a brand logo on their clothing). Typically, however,there is still value to the average person promoting a product in thismanner. Advantageously, by economically and accurately determining theadvertisement value of physical spaces associated with the averageperson, an exemplary embodiment may help open up sponsorshipopportunities for almost anyone.

However, it should be understood that exemplary embodiments maygenerally be used to value almost any type of physical space. To providejust a few examples, advertisement values may be determined for: (a) theback of the screen of a laptop computer, (b) an article of clothing, (c)a billboard, (d) a surface on an automobile, (e) a body surface, (f) aspace on a webpage, (g) a print advertisement space, (h) a surface onproduct packaging, and/or (i) a pet. Many other types of advertisementspaces may also be valued utilizing an exemplary embodiment.

Further, an exemplary methods and systems may be used to value physicalspaces types for various different advertisement formats such as: (a)print advertisements, (b) computer-generated images, (c) video, (d) peeland stick paper advertisements, (e) two-dimensional projections, (f) athree-dimensional projections, (g) iron-on images, (h) temporarytattoos, and/or (i) other advertising formats.

D. Advertisement Marketplace

visibility individuals (e.g., famous athletes and musicians). Suchhigh-visibility individuals are typically the most valuable to anadvertiser, such that the cost of evaluating the sponsorship isjustified by the value to the advertiser. For instance, a famousrace-car driver can readily sell advertising space on his or herclothing or on their car. However, there is no market for the averageperson to do the same (in fact, individuals often pay more to have abrand logo on their clothing). Typically, however, there is still valueto the average person promoting a product in this manner.Advantageously, by economically and accurately determining theadvertisement value of physical spaces associated with the averageperson, an exemplary embodiment may help open up sponsorshipopportunities for almost anyone.

However, it should be understood that exemplary embodiments maygenerally be used to value almost any type of physical space. To providejust a few examples, advertisement values may be determined for: (a) theback of the screen of a laptop computer, (b) an article of clothing, (c)a billboard, (d) a surface on an automobile, (e) a body surface, (f) aspace on a webpage, (g) a print advertisement space, (h) a surface onproduct packaging, and/or (i) a pet. Many other types of advertisementspaces may also be valued utilizing an exemplary embodiment.

Further, an exemplary methods and systems may be used to value physicalspaces types for various different advertisement formats such as: (a)print advertisements, (b) computer-generated images, (c) video, (d) peeland stick paper advertisements, (e) two-dimensional projections, (f) athree-dimensional projections, (g) iron-on images, (h) temporarytattoos, and/or (i) other advertising formats.

B. Advertisement Marketplace

Provided with the ability to determine an advertising value for almostany physical space, there is a need for a marketplace system tofacilitate transactions involving use of such physical spaces asadvertisement spaces. Accordingly, exemplary methods and systems mayhelp to provide an advertisement marketplace.

In an exemplary marketplace, gaze-data-based valuation may be used toprice advertising rights. For instance, in some applications,advertisement rights to an advertisement space may be listed at theadvertisement value, or at a fixed rate that is based on theadvertisement value. In other instances, advertisement rights may belisted at a variable rate. For example, an initial rate may be based onthe advertisement value at or near the time of purchase. The system maythen adjust this rate based on views of the advertisement during theadvertising period (e.g., based on how often the advertisement space isdetected in subsequent gaze data). Other gaze-data-based pricingstructures for advertisement spaces are also possible.

Note that in an exemplary embodiment, the advertisement value upon whichthe listing price is based may be a “relative advertisement value.” Morespecifically, the relative advertisement value may be determinedpre-sale, and thus may differ from the price that the advertisementspace ultimately sells for in an open market. For example, a relativeadvertisement value may be determined for an advertisement space beforeany advertisement is placed, based on gaze data in which the bareadvertisement space is detected. As such, the selling price mayultimately differ based on what the market is willing to pay for theadvertisement space. Thus, in some embodiments the true or officialadvertisement value may be considered to be the market price, and thusmay differ from the relative advertisement value.

In some embodiments, an exemplary marketplace may include variousfeatures to assist a user who wishes to list an advertisement space forsale in the marketplace. For instance, an exemplary system may supportvaluation requests, which allows a user to request and be provided withgaze-data-based advertisement values for the user's advertisementspaces. Such valuation requests may help users evaluate whether or notto list an advertisement space in the marketplace. More specifically,once provided with the advertisement value for their advertisementspace, a user may then elect to list the advertisement space at alisting price that is based on this advertisement value.

Further, an exemplary system may allow a user to sell advertising rightsfor a wide variety of physical spaces. For instance, an exemplary systemmay have pre-defined types of physical spaces that can be valued as anadvertisement space and listed for sale (e.g., higher-visibility typesof physical spaces such as a billboard, the back of a laptop computer,the front of a shirt, etc.). As such, an exemplary system mayautomatically search gaze data for the pre-defined types of physicalspaces, and in so doing may identify potential advertisement spaces.Once a physical space has been identified as a potential advertisementspace, the system may allow a user to sell advertisement rights to theadvertisement space via the advertisement marketplace.

An exemplary advertisement marketplace may additionally or alternativelyallow a user to dynamically define a physical space as an advertisementspace, even if it is not pre-defined as such. For instance, the systemmay provide features via which a user can submit images, video, and/orother information that allows the system to detect when an advertisementspace occurs in gaze data. After receiving such information, the systemcan search for the user-defined advertisement space in gaze data, sothat the advertisement space can be valued and/or listed in theadvertisement marketplace.

In some embodiments, an exemplary system may provide a user who wishesto list advertisement spaces with suggestions of advertisement spacesthat can be listed by the user. In particular, the marketplace systemmay search gaze data (and possibly other data sources as well) forphysical spaces that are associated with the given user and that areusable as advertisement spaces. The system may do so automatically or onrequest by a given user. In either case, the user may then be providedwith suggestions of unlisted advertisement spaces (possibly includingvaluations of each advertisement space), which the user can list in theadvertisement marketplace. This may be particularly useful in thescenario where a user is unaware that a certain physical space is usableas an advertisement space. In this scenario, the suggestions may informa user of the existence of advertisement spaces that the user waspreviously unaware of. Of course, ad-space suggestions may be useful inother scenarios as well.

An exemplary system may also include various features to assist anadvertiser who wishes to purchase an advertisement space. For instance,a marketplace system may allow an advertiser to search, browse, and/orpurchase advertisement spaces that are listed in the marketplace.

Furthermore, an exemplary system may allow an advertiser to identify ordefine an advertisement space for which they are interested inpurchasing advertisement rights. In this regard, the advertiser may beprovided with similar features as those provided to a user wishing tolist an advertisement space, which allow the advertiser to dynamicallydefine a physical space as an advertisement space, and/or to identify apre-defined type of advertisement space. Once an advertisement space hasbeen identified, the marketplace system may identify a user that isauthorized to list the advertisement space, and notify the user thatthere is interest in their advertisement space.

In a further aspect, an exemplary marketplace may include features tofacilitate a transaction to purchase advertisement rights between theuser who listed the advertisement space and the advertiser who ispurchasing the advertisement space. The advertisement marketplace mayalso facilitate completion of the contract created between the sellerand the purchaser after such a transaction. In particular, when anadvertiser purchases an advertisement space, an exemplary marketplacesystem may create and maintain a record of a contract between theadvertiser and user who sold the advertisement. The system may alsoprovide features to facilitate performance of the contract. Forinstance, a server may search gaze data received post-contract todetermine that the advertisement specified by the advertiser is beingdisplayed in the advertisement space in accordance with the contract.Further, the system may facilitate billing the advertiser, and possiblyeven transferring funds between user-accounts for the advertiser and theseller.

Note that herein, when gaze data is said to be associated with a givenuser-account, it should generally be understood that this gaze data wassent by a device that is associated with the given user-account (e.g., adevice that is registered with the user-account). Further, gaze dataand/or other information that is associated with a user-account may alsobe said to be associated with a user since, functionally, associatinggaze data or any other data with a user will generally be accomplishedby associating the data with the user's user account.

In a further aspect, when a user creates a user-account for which a gazevalue may be determined, a user-profile for the user-account may becreated as well. The user-profile may include or provide access tovarious types of information, from various sources, which is related tothe user. For simplicity, examples set forth herein may simply refer toa user-account as including the information included in the associateduser-profile. However, this should not be read as requiring that auser-account include a user-profile. It is possible, in someembodiments, that a user-account may not have an associateduser-profile. Furthermore, herein, the term user-profile may moregenerally be understood to refer to any information or collection ofinformation related to a given user. As such, a user-profile may bespecifically created for a user-account or may simply take the form ofdata that is associated with a given user.

II. Exemplary Methods

FIG. 1 is a flow chart illustrating a method according to an exemplaryembodiment. The method 100 shown in FIG. 1 may be implemented by acomputing device, and in particular, by a server system, in order todetermine a gaze value for a wearable computing device and/or for auser-profile associated with the wearable computing device. According toan exemplary embodiment, the gaze value is based on point-of-view gazedata received from a wearable computing device (which may be referred tointerchangeably as a wearable computing device or a wearable computer).Further, a server system that implements an exemplary method may bereferred to as a gaze-valuation system, as a gaze-valuation server, asan ad-valuation server, or simply as a server system or server.

As shown by block 102, method 100 involves a gaze-valuation serverreceiving gaze data for a first wearable computing device, which isassociated with a first user-account. The server may analyze the gazedata from the first wearable computing device to detect occurrences ofadvertisement spaces in the gaze data, as shown by block 104. Then,based at least in part on the detected ad-space occurrences, the servermay determine an individual gaze value for the first user-account, asshown by block 106. The server may then send a gaze-value indication,which indicates the individual gaze value, to the first user-account, asshown by block 108.

In an exemplary method 100, the gaze data received for a given wearablecomputing device is generally indicative of the wearer-view associatedwith the wearable computing device. For example, server may receive gazedata from a wearable computing device in the form of point-of-view (POV)video that is captured at the wearable computing device. As such, thePOV video may be monitored in order to detect when advertisement spacesoccur in the video.

In an exemplary method, such as method 100, gaze data may additionallyor alternatively take forms other than point-of-view video. For example,the gaze data may take the form of point-of-view images captured by aforward- or outward-facing camera on a wearable computing device oranother device. As a specific example, a given wearable computing devicemay periodically take a picture using a camera on an HMD that isgenerally aligned with the wearer's field of view. The wearablecomputing device may send these periodically-captured pictures to theserver system for use in an exemplary method. Other examples are alsopossible.

Since the gaze data from a given wearable computing device is generallyindicative of the wearer-view of the wearable computing device's wearer,the gaze data may be interpreted to be generally indicative of what thewearer of the device is actually looking at. For instance, the gaze datamay be analyzed to determine information such as when a wearer islooking at a particular ad space and/or how long the wearer was lookingat the particular ad space, among other information. Accordingly, thegaze data may be used to determine a gaze value for the wearer.

In an exemplary embodiment, the individual gaze value for a user (whichmay also be referred to as the gaze value) is indicative of the value ofthe user's gaze to advertisers. More specifically, the value of theuser's gaze may represent the cumulative value to advertisers of theuser viewing advertisements over time. As such, determining theindividual gaze value may involve determining what each view is worth toan advertiser (e.g., what each occurrence of an ad space in gaze dataassociated with the given user is worth) and calculating a total valuefor all of the user's views.

Exemplary systems will now be described before further details ofexemplary methods are set forth.

III. Exemplary Server Systems

FIG. 2 is a simplified block diagram illustrating a communicationnetwork via which gaze data may be received, according to an exemplaryembodiment. As shown, communication network 200 includes a number ofwearable computing devices 202A to 202D, which are configured tocommunicate with a server system 204 via one or more networks 206. Anexemplary network, such as communication network 200, may also includecomputing devices other than wearable computing devices, such as laptopcomputer 203 and mobile phone 205, for instance. As such, an exemplaryadvertisement marketplace may be implemented in a network such ascommunication network 200.

In order to facilitate an exemplary method, the users of wearablecomputing devices 202A to 202D may register their respective devices andopt in to programs via which the users submit gaze data from theirrespective devices. As such, wearable computing devices 202A to 202D maysend gaze data to the server system 204, which the server system 204 maythen analyze to help determine advertisement values for advertisementspaces, possibly to valuate advertisement spaces as well. Further, insome embodiments, laptop 203, mobile phone 205, and/or other computingdevices may provide supplemental gaze data, which may be used by serversystem 204 to supplement the gaze data from wearable computing devices202A to 202D.

In an exemplary embodiment, the server system 204 may be a computingsystem including one or more computing devices. In particular, serversystem 204 may be a cloud-based server system that is configured toreceive gaze data, and to determine a gaze value for at least one ofwearable computing devices 202A to 202D. In a further aspect, the serversystem 204 may also be configured to utilize the gaze data to valueadvertising spaces and/or support an advertisement-space marketplace foradvertising spaces.

As noted, the gaze data in an exemplary embodiment may includepoint-of-view videos captured by a number of wearable computing devices.For example, some or all of the wearable computing devices 202A to 202Dmay include or take the form of glasses-style HMDs that each include aforward-facing video camera for taking point-of-view video (e.g., videothat generally captures the perspective of a person wearing the HMD). Assuch, when the HMD is worn, the forward-facing camera will capture videoand/or images that are generally indicative of what the wearer of theHMD sees. Note that exemplary glasses-style HMDs will be described ingreater detail with reference to FIGS. 7A, 7B, 8A, 8B, and 9.

Further, server system 204 may include or be in communication with anad-valuation server 212 and an ad-marketplace server 214. In someembodiments, ad-valuation server 212 and ad-marketplace server 214 maybe separate server systems, which each include one or more computingdevices. In other embodiments, some or all of the functionalityattributed to ad-valuation server 212 and ad-marketplace server 214 maybe provided by a single server system, which may include one or morecomputing devices.

In an exemplary embodiment, ad-valuation server 212 may be configured toreceive gaze data from wearable computing devices 202A to 202D. Further,ad-valuation server 212 may analyze the received gaze data foroccurrences of one or more ad spaces, and generate wearer-view data forthe ad spaces based on occurrences of the ad spaces in the gaze data.

In a further aspect, the server system 204 may include or have access toa wearer-view database 208 that includes wearer-view data for a numberof advertisement spaces (e.g., ad spaces indicated by ad-space database210). When ad-valuation server 212 generates wearer-view data,ad-valuation server 212 may store the generated data in wearer-viewdatabase 208. Accordingly, server system 204 may access the wearer-viewdatabase 208 to retrieve wearer-view data for a given ad space, which inturn may be used to determine the ad value for the given ad space.

To assist the server in detecting occurrences of various ad spaces ingaze data, advertisement server 204 may include or have access to anad-space database 210 that includes information that can be used toidentify various ad spaces. Accordingly, ad server system 204 may usethe identifying information from ad space database 210 to determine whenad spaces occur in gaze data from wearable computing devices 202A to202D. Further, in embodiments that utilize location data for ad spaces,ad-space database 210 may also store location information for individualad spaces.

In another aspect, ad-valuation server 212 and/or other components ofsystem 204 may additionally or alternatively be configured to use gazedata to determine an individual gaze value for a given user-account. Assuch, ad-valuation server 212 and/or other components of system 204 mayinclude program instructions stored in a tangible computer-readablemedium that are executable to provide the functionality describedherein, and possibly to provide other functionality as well.

In another aspect, server system 204 may include ad-marketplace server214, which is configured to provide an advertisement marketplace viawhich advertisement spaces that are valued by ad-valuation server 212can be bought and sold. Further, ad-marketplace server 214 mayfacilitate transactions between parties in such an advertisementmarketplace. As such, computing devices such as wearable computingdevices 202A to 202D, laptop computer 203, and/or mobile phone 205, maybe provided with advertisement marketplace functions via the serversystem 204, and in particular, via ad-marketplace server 214.

Data related to advertisement space in the advertisement marketplace maybe stored in ad-space database 210. For instance, ad-marketplace server214 and/or other components of server system 204 may support anadvertisement marketplace including features for listing advertisementrights to an advertisement space for sale, a bidding system for physicalspaces (e.g., auction functionality), organization and indexing ofavailable physical spaces, searching and/or browsing listings foradvertisement spaces, tracking usage of advertisement spaces,calculation of fees and other billing functions, and/or portfoliomanagement for sellers and purchasers of advertisement space, amongother features.

Further, server system 204 may provide access to an advertisementmarketplace via a website, via a standalone application, and/or viaother points of access. Data related to listings and/or transactions inthe advertisement marketplace may then be stored in ad-space database210.

IV. Detecting Advertisement Spaces in Gaze Data

As noted above, an exemplary method 100 may involve analysis of gazedata to detect when advertisement spaces occurs in the gaze data. To doso, an exemplary server system 200 may employ various types of videoand/or image-processing techniques. For instance, advertisement serversystem 204 may implement various well known and yet-to-be-developedtechniques for object recognition in video and/or still images in theprocess of recognizing advertising spaces.

In some cases, an ad space may be identified in gaze data by way of theadvertisement that is displayed in the ad space. For example, ad-spacedatabase 210 may include data related to which specific advertisementsare being displayed in which ad spaces (and may further indicate whenthere is no advertisement being displayed in a given ad space). As such,ad-valuation server 212 may search for advertisements that are currentlybeing displayed in gaze data. To do so, the ad-valuation server may uservarious visual search techniques that are now known or yet to bedeveloped in order to identify an advertisement in gaze data.

In other cases, an ad space may itself be identified, withoutnecessarily relying on the particular ad that is being displayed in thead space. (Note that this functionality may be particularly useful incases where an ad space is empty.) In such an embodiment, detecting thatan advertisement space occurs in gaze data may involve recognizing whenthe gaze data includes an object or a certain combination of objectsthat are associated with a particular advertisement space. For example,to recognize advertisement space on the bumper of a particular car, gazedata may be analyzed for an object shaped and/or having coloration thatis characteristic of a car bumper. Further, the gaze data may beanalyzed for an object having such a shape and/or coloration inconjunction with a license plate having a certain license plate number.In such an embodiment, the server system may consider an occurrence of abumper in combination with the license plate number for the specific carto be an occurrence of the ad space on the car's bumper. Many otherexamples are also possible.

In some cases, searching gaze data from a large number of wearablecomputing devices for a large number ad spaces may be data intensive.Accordingly, an exemplary server and or wearable computing devices mayimplement pre-processing techniques to tag and ID certain types ofobjects or certain types of information in gaze data, which may help tospeed up the process of detecting ad spaces. In some instances, wearablecomputing devices and/or the server may also store gaze data forprocessing when, e.g., a wearable computing device is offline, or whenthe amount of real-time data being collected is generally less. Forexample, a server may use certain processing resources to receiveincoming gaze data during the day, when more gaze data may be received,and then re-assign these processing resources to analyze stored gazedata for ad spaces at night, when less new gaze data may be received.

In some embodiments, a server system may utilize location data to detectan occurrence of an ad space in gaze data. For example, a server systemmay determine or be provided with the geographic location of aparticular ad space (e.g., the GPS coordinates of the ad space). Then,when the ad space is detected in gaze data from a particular wearablecomputing device, the server may determine the location of the wearablecomputing device. If this wearable computing device is located such thatthe ad space could be visible to the wearer of the wearable computingdevice (e.g., within a predetermined distance from the location of thead space), then the server system may consider this an occurrence of thead space. However, if the wearable computing device that provided thegaze data is located such that the ad space could not be viewed by thewearer (e.g., not within a predetermined distance from the location ofthe ad space), then the server system may not consider this anoccurrence of the ad space.

As another example, a server system may use the geographic location of aparticular ad space to limit the gaze data that is monitored for the adspace. For instance, the server may determine the locations of wearablecomputing devices from which gaze data is received. As such, the servermay only monitor gaze data that is received from wearable computingdevices that are located within a predetermined distance from the adspace. Other methods that utilize the location of an ad space whendetecting occurrences of the ad space in gaze data are also possible.

In some embodiments, radio frequency identification (RFID) may be usedto help detect occurrences of an ad space in gaze data. In particular,an ad space may be associated with a certain RFID tag, and wearablecomputing devices may be configured with RFID readers. As such, when awearable computing device detects an RFID tag from an ad space, thewearable computing device may relay this to the server system. Forinstance, when the wearable computing device detects an RFID that isassociated with an ad space, it may insert metadata into the gaze datawhich indicates the RFID tag and the time at which the RFID tag wasdetected. Alternatively, the wearable computing device may send aseparate message indicating that the RFID tag was detected at aparticular time. In either case, the server system can then search forthe associated ad space in gaze data that is received from the wearablecomputing device at or near the time when the RFID tag is detected. Thismay help the server system to more efficiently detect occurrences of adspaces, as the timing with which the RFID tags are detected mayindicate, for example, times in corresponding point-of-view video wherethe ad space is likely to occur. Further, various types of RFID may beutilized, such as near-field communications (NFC) and/or other types ofRFID, depending upon the implementation.

In some embodiments, barcodes may be used to help detect occurrences ofan ad space in gaze data. For instance, a barcode that identifies an adspace may be displayed within or near to an ad space. The server systemmay then search for barcodes within gaze data. When a barcode associatedwith a particular ad space is detected, the server may consider this tobe an occurrence of the ad space, or may treat this as a factor that,along with other factors, can indicate that there is an occurrence ofthe ad space in the gaze data. Various types of barcodes, such as highcapacity color barcodes (HCCBs) and/or quick response (QR) codes may beutilized in such an embodiment. Other types of barcodes are possible aswell.

It should be understood that the above techniques for detectingoccurrences of ad spaces are not intended to be limiting. Othertechniques are also possible.

V. Determining a Gaze Value

As noted in reference to block 106 of FIG. 1, an exemplary method 100may involve a wearable computing device and/or a server systemdetermining an individual gaze value for a given user-account, based onadvertisements that are detected from a device or devices associatedwith the user-account. The gaze value may be determined using a numberof different techniques, which may vary from implementation toimplementation.

In a basic embodiment, the server system may value each view of anadvertisement space equally, without regard to who is viewing the adspace, how long they view it, what ad (if any) is being displayed in thead space, or the characteristics of the ad space itself. In thisscenario, each occurrence of an ad space may be valued equally.Therefore, the server system may determine the gaze value for a givenuser-account by determining the total number of ad-space occurrencesduring the period over which a user's gaze value is being calculated,and multiplying the total number by a universally-defined value for asingle view.

In other embodiments, the server system may determine an individualgaze-value contribution for each occurrence of an ad space. As such, theindividual gaze-value contribution may vary from occurrence tooccurrence, depending upon which user is viewing the ad space, how longthe user views the ad space, characteristics of an ad that is beingdisplayed in the ad space, characteristics of the ad space itself,and/or other factors.

A. Gaze Value Based on Per-Occurrence Gaze-Value Contributions

FIG. 3A is a flow chart illustrating a method for determining gazevalue, according to an exemplary embodiment. In particular, FIG. 3Aillustrates a method 300 in which the gaze value for a givenuser-account is based on individual gaze-value contributions ofoccurrences of the ad space in gaze data associated with the givenuser-account.

More specifically, method 300 involves monitoring gaze data that isassociated with a given user account for occurrences of advertisementspaces, as shown by block 302. Each time an advertisement space isdetected, as shown by block 304, the server system may determine agaze-value contribution of the occurrence, as shown by block 306. Theserver system may further determine gaze-value contributions for anumber of occurrences by repeating blocks 304 and 306 as additionalad-space occurrences are detected. The server system may then use thegaze-value contributions for the detected occurrences as a basis fordetermining the individual gaze value for the user-account, as shown byblock 308.

In some embodiments, such as method 300 of FIG. 3A, the gaze-valuecontribution for each occurrence may be determined in real-time as eachoccurrence is detected. However, it should be understood that thegaze-value contribution for some or all occurrences of an ad space maybe calculated at a later time, based on wearer-view data that is storedas the occurrences are detected in the gaze data.

A server may use various techniques to determine the individualgaze-value contribution for a given occurrence of an ad space in gazedata. Such techniques may utilize various different factors to determinethe gaze-value contribution for a given occurrence. In some embodiments,the server may determine a weighting value for an occurrence, which maythen be applied to a “standard” gaze-value contribution to determine thegaze-value contribution for the particular occurrence. In such anembodiment, the weighting value may be based on various factors orcombinations of factors, such as the particular wearable computingdevice from which the gaze data including the particular occurrence wasreceived, the duration of the occurrence, characteristics of the personwho viewed (e.g., as indicated by the user-profile associated with theoccurrence), the focus value of the occurrence, and/or other factors.

Once a server system has determined the individual gaze-valuecontributions for a number of occurrences, the server may use varioustechniques to determine the gaze value for the user. For example, insome embodiments, the server may determine the gaze value by summing thegaze-value contributions of some or all of the detected occurrences. Asanother example, in some embodiments, the server may determine the gazevalue by averaging the gaze-value contributions of some or all of thedetected occurrences. Other examples are also possible.

In some cases, the gaze-value contribution for each occurrence of an adspace may be a dollar amount that is attributed to the occurrence. Assuch, the server may determine a dollar amount for the gaze value bysumming the gaze-value contributions. In other cases, the gaze-valuecontribution for each occurrence of the ad space may be a price rate(e.g., dollars per month, dollars per week, etc.) that is attributed tothe occurrence of the ad space. As such, the server may determine the advalue by summing or averaging the gaze-value contributions to get atotal or an average price rate, respectively. Other examples are alsopossible.

It should be understood that techniques described herein for determiningan individual gaze value and/or determining gaze-value contributions ofindividual occurrences of an ad space are not intended to be limiting.Other techniques for determining an individual gaze value and/ordetermining gaze-value contributions of individual occurrences are alsopossible, without departing from the scope of the invention.

B. Gaze-Value Contribution Based on Duration

As noted, the server system may consider the duration of an occurrencewhen determining the portion of the ad value that is attributable to agiven ad-space occurrence associated with a given user-account. In someembodiments, a predetermined rate may be defined for gaze valuation(e.g., dollars per minute), and this rate may be used in conjunctionwith the duration of an occurrence to determine the gaze-valuecontribution of the occurrence (e.g., by multiplying the rate by theduration).

In other embodiments, the server system may determine a total view timefor an ad space by summing the respective durations of all occurrencesassociated with a number of different user-accounts (e.g., alluser-accounts that have authorized collection and use of gaze data forsuch a purpose). In some cases, the server system may extrapolate fromthe gaze data to estimate a total view time for all views of an ad space(whether captured in gaze data or not). In either case, the ad-valueportion that is attributable to the given occurrence may be based on theratio of the duration of the occurrence to the total view time. Forinstance, if an ad space is valued at a rate of $12 per day, and theserver estimates that an ad space averages two hours of viewing per day,a $0.10 portion may be attributed to an occurrence lasting for oneminute. Other examples are also possible.

In a further aspect, when a gaze-value contribution of an occurrenceaccounts for the duration of the occurrence, it may also take intoaccount a diminishing return of viewing duration. For example, if aperson views an advertisement for one minute, the first twenty secondsthat a person views the advertisement may be considered more valuablethan the next twenty seconds during which the person continues to viewthe advertisement, which in turn may be considered more valuable thanthe final twenty seconds in the minute-long view. Similarly, having auser view one advertisement may be considered less valuable overall thaneither the same user view five advertisements for one minute each orfive different users viewing the same or different advertisements forone minute each. As such, when duration is considered the diminishingreturns of extended viewing periods may be taken into account.

C. Gaze-Value Contribution Based on Focus Value for an Occurrence

While detecting an ad space in gaze data from a wearable computingdevice may generally indicate that the ad space was within the field ofview of the wearer, it is possible that the ad space was in theperiphery of the wearer's field of view, was in the center of thewearer's field of view, or somewhere in between. Furthermore, the wearercan move their eyes such that they are focusing on the ad space or suchthat they are focusing elsewhere in their field of view. Accordingly, insome applications, an exemplary method may vary the gaze-valuecontribution for a given occurrence based on the amount of attentionpaid to the ad space during the occurrence. Generally, the higher thefocus value for a given occurrence, the higher the gaze-valuecontribution of a given occurrence. However, there may be exceptions tothis general principal, without departing from the scope of theinvention.

An exemplary server system may use various techniques to calculate afocus value for a given occurrence of an ad space in gaze data. In someimplementations, the focus value may be a numeric value that increasesas more attention is paid to an ad space. However, in alternativeimplementations, it is possible that the focus value may be inverselyproportional to the amount of attention paid to the ad space.

In some embodiments the focus value may be based at least in part on alocation of the ad space in the gaze data. For example, consider anembodiment where the gaze data from a given wearable computing deviceincludes POV video from the device. The server system may determine thelocation of the advertisement space in the point-of-view video, and thenuse the location of the advertisement space in the point-of-view videoas a basis to determine the focus value. For instance, the server systemmay determine coordinates of the ad space (e.g., the coordinates of thecenter of the ad space) within one or more video frames. The server maythen use the determined coordinates as input when determining a focusvalue for the detected occurrence. In particular, the closer thelocation of the advertisement space is to the center of the video frame,the greater the determined focus value, and vice versa. Thus, inpractice, the server may increase the focus value as the distancebetween the location of the ad space and the center of the video framedecreases.

Note that if multiple frames with the object are used to calculate thelocation of the ad space, the server system may determine thecoordinates of the object in each frame and then average the coordinatesfrom the frames to determine the location of the object in the videoframe. In some embodiments, the server may implement Visual SimultaneousLocalization and Mapping (V-SLAM) to track the location of an objectfrom frame to frame. V-SLAM can provide a registered 3D point cloud ofthe world, which identifies the general shape of objects and therespective distances to the objects, and allows for pixels in one frameto be related to pixels in another frame. V-SLAM is well known in theart and therefore is not discusses in further detail herein.Furthermore, it should be understood that other techniques may also beutilized instead of or in addition to V-SLAM.

In some embodiments, the server may utilize eye-tracking datacorresponding to the occurrence of the ad space when determining a focusvalue for the occurrence. The eye tracking data may generally beindicative of a direction that the wearer is looking. Accordingly, theserver may determine the location of the advertisement space in thepoint-of-view video, and then use eye-tracking data from the wearablecomputing device that provided the gaze data to determine a wearer-gazelocation in the point-of-view video at or near the time when the adspace occurs in the video. The server may then determine the proximityof the wearer-gaze location to the location of the advertisement spacein the point-of-view video, and use the proximity as a basis fordetermining the focus value for the particular occurrence of theadvertisement space. In an exemplary embodiment, the server maygenerally increase the focus value as the distance between the ad-spacelocation and the wearer-gaze location decreases.

In some embodiments, the server may use movement of the ad space duringa given occurrence in POV video as a basis for determining the focusvalue for the occurrence. For instance, if an ad space is detected in anumber of consecutive video frames, the server may determine thelocation of the ad space in each of the frames (or possibly in arepresentative subset of the frames in which the ad space is detected).The server may then compare the determined locations of the ad space todetermine how much the ad space moved during the occurrence of the adspace. If the ad space is relatively still and does not movesignificantly within the frame, this may be indication that the wearerfocusing on the ad space. On the other hand, more movement of the adspace may indicate that the wearer is focusing on something other thanthe ad space. Accordingly, the server may generally increase the focusvalue of the occurrence as the amount of movement during the occurrencedecreases, and vice versa.

In some embodiments, the server may use the amount of the point-of-viewvideo frame that is occupied in POV video as a basis for determining thefocus value for the occurrence. In particular, if the ad space occupiesa large amount of the video frame, this may be an indication that the adspace is more prominent in the wearer's field of view and/or that theuser is closer to the ad space. As such, if the ad space occupies alarge amount of the video frame, the server may interpret this as anindication that the user is paying more attention to the ad space.Accordingly, the server may generally increase the focus value as thepercentage of the video frame that is occupied by the ad spaceincreases.

It should be understood that the focus value may be based on just one ofthe above factors or another factor altogether. Further, the focus valuemay also be based on a combination of some or all of the above factorsand/or other factors.

D. Gaze-Value Contribution Based on User-Characteristics.

In some embodiments, an exemplary method may help account for the factthat views of an ad space by certain people may be considered morevaluable than views of the same ad space by other people. As such,various types of information provided by and/or related to a givenuser-account may be used to determine a gaze-value contribution for anad-space occurrence in gaze data for the given user-account.

For instance, a user-account may include or provide access to: (a)consumer information such as spending habits, locations of purchases,amounts of purchases, types or categories of purchases, timing ofpurchases, etc., (b) demographic information such as age or age group,ethnicity, nationality, sex, location of residence, and/or location ofworkplace, (c) contact and/or social networking information such as auser's contacts, and possibly data indicating a purchasing influence ofthe user with regard to their contacts (e.g., data indicating anycorrelation of the user's purchasing history to the wearers' friends'purchasing histories), and/or (d) other information such as income, jobor job type, other job details, hobbies, interests, and so on. When auser has given permission for information from their user-account to beused for purposes of determining their individual gaze value, the serversystem may use such information to determine the gaze-value contributionthat is attributable to a given occurrence of an ad space in gaze dataassociated with the user's account.

To provide one specific example, the server may determine an incomelevel for a given user-account and then increase or decrease thegaze-value contribution for occurrences in the user's gaze dataaccording to their income level. For example, number of income rangesmay be mapped to certain adjustments that should be applied whendetermining gaze-value contribution. The adjustments may generallyincrease the gaze-value contribution for higher income ranges, anddecrease the gaze-value contribution for higher income ranges.

In another application, the server may determine an average income levelfor a relevant group (e.g., users who have viewed the advertisement, atarget user group for the ad displayed in the ad space, the populationas a whole, etc.). The server may then adjust gaze-value contributionfor an occurrence in gaze data associated with the user based on therelationship between the user's income level and the average incomelevel (e.g., whether and/or by how much the user's income level is aboveor below the average income level). Other examples are also possible.

Furthermore, in some applications, the gaze-value contribution for anoccurrence may not increase or decrease in proportion to associateduser's income level. For example, a given advertisement may be targetedat a specified income range. As such, views by users that do not fallwithin the specified income range may be considered less valuable.Therefore, gaze-value contributions for user-accounts having an incomelevel outside the income range may be reduced, regardless of whether theincome level is above or below specified income range. Other examplesare also possible.

As another specific example, the server may base the gaze-valuecontribution on how well demographic information from a user-profilematches a targeted demographic profile for the advertisement displayedin a detected ad space. The targeted demographic profile may indicate asingle type of demographic information to be evaluated (e.g., male orfemale) or a combination of various types of demographic information(e.g., males between 30 and 45 years of age who live in an urbanlocation). Other examples are also possible.

E. Gaze-Value Contribution Based on Context

In a further aspect, an exemplary method may help account for the factthat in some cases, a certain user viewing a certain advertisement inone context, may be considered more valuable than the same user viewingthe same advertisement in another context. Accordingly, the gaze-valuecontribution for a given advertisement space that is detected in auser's gaze data may vary based on the advertisement that is displayedand context associated with detecting the advertisement space.

For instance, consider a user-profile that indicates the particular useris a scientist who is interested in the latest research in their field.Therefore, it may be determined that this user will likely be interestedin an advertisement for an upcoming conference in their field. As such,gaze-value contributions of certain advertisements may be adjusteddepending on whether or not the context in which the advertisement isviewed makes it more or less likely that this user will be interested inthe advertisement and/or be prompted to act when they view theadvertisement.

As a specific example, consider a scenario where the above-describeduser is driving to work at their laboratory, and sees a billboard whiledriving. In this scenario, context signals such as the day of the week,time, and/or the user's location may be used to determine that the useris “driving to work at their lab” (e.g., based on context signalsindicating that the user is located on a highway on the laboratory, on aweekday, at 8:00 am). If the advertisement for upcoming conference inthe user's field is displayed on the billboard in this context, it maybe inferred that the user is likely to have work on their mind, and thusbe more likely to be interested in and/or to act on this advertisement.However, if this same advertisement is displayed to the same user whenthe user is on vacation, context may be used to reduce the gaze-valuecontribution of this user viewing the advertisement.

In a further aspect, gave-value contribution in a given context may varybetween different users (e.g., between different user-accounts). Forexample, in some cases, the gaze-value contribution corresponding to afirst user viewing a certain advertisement in a certain context may begreater than the gaze-value contribution for another user viewing thesame advertisement in same context. In particular, the relationshipbetween characteristics of a user indicated by their user-account and agiven context may be evaluated to determine whether an adjustment to agaze-value contribution is appropriate in the given context.

For example, consider a first user and a second user who are both at adepartment store. An exemplary system and/or the users' respectivedevices may evaluate various context signals, such as each user'slocation (e.g., at the location of the department store), the time ofday (e.g., a time during the business hours of the department store),and so on, to determine that the first user's context is “at thedepartment store.” However, employment information in the first user'suser-account may indicate that the first user is an employee of thedepartment store, while employment information (and other types ofinformation) in the second user's user-account may reveal no suchconnection to the department store.

Note also that in some instances, context signals may be used to infercharacteristics of a given user, such that use of information from auser-account may not be necessary. For instance, in the above example,it might be inferred that the first user is an employee of thedepartment store from context signals that indicate, e.g., that thefirst user is typically located in the department store for eight hoursper day, five days per week, during store business hours. Other examplesare also possible.

Based on these relationships between the context and the informationfrom the respective user characteristics of these users, the gaze-valuecontribution for the first user viewing a particular advertisement whilelocated in the department store may be lower than the gaze-valuecontribution for the second user viewing the same advertisement whilelocated in the department store. More specifically, the gaze-valuecontribution for the first user may be lower because the first user isan employee and thus may be considered unlikely to act on anadvertisement while on the job, whereas the second user may be inferredto be shopper at the department store, since available information doesnot indicate to the contrary. This may be the case even if most or allother information regarding the two users is the same (e.g., the same orsimilar demographic information, same income range, etc.).

To determine a context associated with a given advertisement beingdetected in gaze data for a given user-account, a cloud-based server maybe configured to use context data from a single device that isassociated with the user-profile. For instance, referring back to FIG.2, server system 204 may use context data from a single device, such aswearable computing device 202A, to determine context for theuser-account that is associated with that device. Alternatively, theserver may be configured to aggregate context data from two or moredevices that are associated with the user-account, and use the aggregatecontext data to determine context for the user-account. For example, ifa number of devices are all associated with the same user-account, suchas wearable computing device 202A, a mobile phone, and a laptopcomputer, for example, then context data provided by some or all of theassociated devices may be aggregated when determining context for theassociated user-profile.

In an exemplary embodiment, the context associated with a givenuser-profile may be determined using various techniques. In general, a“context” may be determined based on various “context signals” orcombinations of context signals. A context signal may be any signal thatprovides a measurement or otherwise provides information pertaining tothe state or the environment associated with a certain subject (e.g.,with a certain user, device, event, etc.). In this case, the contextsignals associated are generally pertain to a user-profile for a wearerof a wearable computing device. As such, the context signals maygenerally provide some type of information pertaining to the state orthe environment of the wearer.

In some instances, a context may be a state associated with a particularcontext signals or set of context signals. However, a context may alsobe abstracted from the context signals upon which it is based. As such,a “context” may also be a data-based description or characterization ofan environment or state that is determined or derived from one or morecontext-signals. For example, contexts may take the form of dataindicating environment or state information such as “at home,” “atwork,” “in a car,” “indoors,” “outside,” “in a meeting,” etc.Furthermore, a context may be a qualitative or quantitative indicationthat is determined based on one or more context signals. For example,context signals indicating that that it is 6:30 AM on a weekday and thata user is located at their home may be used to determine the contextthat the user is “getting ready for work.”

Many types of information, from many different sources, may be used ascontext signals or provide information from which context signals may bederived. For example, context signals may include: (a) the current time,(b) the current date, (c) the current day of the week, (d) the currentmonth, (e) the current season, (f) a time of a future event or futureuser-context, (g) a date of a future event or future user-context, (h) aday of the week of a future event or future context, (i) a month of afuture event or future user-context, (j) a season of a future event orfuture user-context, (k) a time of a past event or past user-context,(l) a date of a past event or past user-context, (m) a day of the weekof a past event or past user-context, (n) a month of a past event orpast user-context, (o) a season of a past event or past user-context,ambient temperature near the user (or near a monitoring deviceassociated with a user), (p) a current, future, and/or past weatherforecast at or near a user's current location, (q) a current, future,and/or past weather forecast at or near a location of a planned event inwhich a user and/or a user's friends plan to participate, (r) a current,future, and/or past weather forecast at or near a location of a previousevent in which a user and/or a user's friends participated, (s)information on user's calendar, such as information regarding events orstatuses of a user or a user's friends, (t) information accessible via auser's social networking account, such as information relating a user'sstatus, statuses of a user's friends in a social network group, and/orcommunications between the user and the users friends, (u) noise levelor any recognizable sounds detected by a monitoring device, (v) itemsthat are currently detected by a monitoring device, (w) items that havebeen detected in the past by the monitoring device, (x) items that otherdevices associated with a monitoring device (e.g., a “trusted”monitoring device) are currently monitoring or have monitored in thepast, (y) information derived from cross-referencing any two or more of:information on a user's calendar, information available via a user'ssocial networking account, and/or other context signals or sources ofcontext information, (z) health statistics or characterizations of auser's current health (e.g., whether a user has a fever or whether auser just woke up from being asleep), and (aa) a user's recent contextas determined from sensors on or near the user and/or other sources ofcontext information, (bb) a current location, (cc) a past location, and(dd) a future location, among others. Those skilled in the art willunderstand that the above list of possible context signals and sourcesof context information is not intended to be limiting, and that othercontext signals and/or sources of context information are possible inaddition, or in the alternative, to those listed above.

In some embodiments, the detection or observation of a certain event indata from a certain data source may itself be interpreted as a contextsignal. For example, the fact that a certain word is detected in anaudio signal from a microphone may be interpreted as a context signalproviding context to the event of that word being spoken. Other examplesare also possible.

In some embodiments, context signals may be obtained or derived fromsources such as a user's computer-based calendar, blog, webpage, socialnetwork account, and/or e-mail account, among others. For instance,context signals may be provided by user's calendar entries, e-mailmessages, and social-network profile, messages, posts, and/or tweets.Further, in some embodiments, similar context signals may be obtained orderived from other users' computer-based calendars, blogs, webpages,social network accounts, and/or e-mail accounts, who are listed in auser's electronic contact list, listed as a “friend” in a user's socialnetwork, or otherwise associated with the user (provided such users haveopted in to share such context information).

It should be understood that the above examples of contexts, contextsignals, techniques for determining a context, and/or techniques forusing context when selecting an advertisement are provided forillustrative purposes, and are not intended to be limiting. Otherexamples and/or techniques are also possible.

F. Combining Various Factors to Determine a Gaze Value

FIG. 3B is a flow chart illustrating a method for using multiple factorsto determine an individual gaze value for a user-account, according toan exemplary embodiment. In method 350 of FIG. 3B, a pre-determined basecontribution for an ad space is weighted according to multiple factorsin order to determine a gaze-value contribution for each occurrence ofan ad space in gaze data for a given user-account. The individual gazevalue for the user-account may then be determined from the collectiveknowledge provided by the gaze-value contributions of the ad-spaceoccurrences detected in gaze data associated with the user-account.Method 350 is described by way of example as being implemented by aserver system, but could also be implemented by a wearable computingdevice, by another device or system, or by a combination of devices andsystems.

Method 350 involves the server system determining a weighting value(weighting_value_(i)) to be applied for an occurrence of an ad space(O_(i)) in gaze data from a given user account, based on m differentfactors (f_1 _(i) to f_m_(i)). In particular, the server system maydetermine a value for a first factor (f_1 _(i)) with respect to a givenoccurrence of an ad space in gaze data for a first user-account, asshown by block 352. The server system may then determine an averagevalue for the first factor (avg_f_1 _(i)), as shown by block 354. Assuch, the server system may determine a relationship between thedetermined value for the given occurrence and the average value withrespect to the first factor (e.g., f_1 _(i)/avg_f_1 _(i)), as shown byblock 356. If there are additional factors to consider (e.g., if m isgreater than one), as indicated by block 358, then the server may repeatblocks 352 to 356 for the additional factors, until the relationshipbetween the given user-account and relationship between the value forthe given occurrence and the average value has been determined for eachfactor. Once all the factors have been evaluated, the server system mayuse the determined relationships as a basis for determining a weightingvalue for the occurrence, as shown by block 360.

Thus, for a given occurrence O_(i), and values for a set of n factorsf_1 to f_n, method 350 may be implemented to determine aweighting_value_(i) as a function of the respective relationshipsbetween the values of factors for the given occurrence f_1 _(i) tof_m_(i) and the average value for factors avg_f_1 _(i) to avg_f_m_(i).For example, weighting_value_(i) may be calculated as:weighting_value_(i) =F[(f_1_(i)/avg_f_1_(i)),(f_2_(i)/avg_f_2_(i)), . .. (f_m _(i)/avg_f_m _(i))]Note that the particular function used may vary from implementation toimplementation, depending upon the design goals.

Once the server has determined the weighting_value_(i) for a givenoccurrence O_(i) of an ad space, the server may use a base contributionfor the ad space in the ad space detected in occurrence O_(i) tocalculate the gaze value contribution for the occurrence as:gaze_value_contribution_(i)=base_contribution_(i)*weighting_value_(i)

Further, the server system may repeat the above process to determine agaze-value contribution for n occurrences O_(i) in the gaze data for theuser-account. As such, the individual gaze value for the user-accountmay be determined as a function of gaze_value_contribution_(i) for iequal 1 to n. For example, the individual gaze value may be calculatedas the sum of gaze_value_contribution₁ to gaze_value_contribution_(n).Other examples are also possible.

It should be understood that many other types of information provided byand/or related to a given user-account may be considered, alone or incombination, when determining the gaze-value contribution of an ad spacein gaze data for the given user-account. Further, information providedby and/or related to a given user-account may be considered incombination with other factors, such as duration of an occurrence and/ora focus value associated with the occurrence, when determining thegaze-value contribution for an occurrence.

G. Fitting Gaze-Value Contributions to Predetermined Ad Values

In some of the described embodiments, the gaze-value contribution for agiven occurrence of an ad space may be calculated as the portion of aknown ad value for the ad space that is attributable to the givenoccurrence.

FIG. 4 is a flow chart illustrating a method for determining gaze value,according to an exemplary embodiment. In particular, FIG. 4 illustratesa method 400 in which the gaze-value contribution for each occurrence ofan ad space is a portion of the ad value that is attributable to theparticular occurrence.

More specifically, method 400 involves monitoring gaze data from atleast one wearable computing device associated with a given user account(and possibly other devices associated with the same user-account) foroccurrences of advertisement spaces, as shown by block 402. Each time anoccurrence of an advertisement space is detected, as shown by block 404,the server system may determine an advertisement value of theadvertising space, as shown by block 406. Further, the server system maydetermine a portion of the advertisement value that is attributable tothe occurrence of the advertisement space in the gaze data associatedwith the first user-account, as shown by block 408. The first serversystem may repeat block 404 to 408 for a number of occurrences ofadvertisement spaces to determine portions of the respectiveadvertisement values that are attributable to the respective occurrencesnumber of ad-space occurrences. Accordingly, the server system may usethe determined advertisement-value portions as a basis for determiningan individual gaze value for the given user-account, as shown by block410.

In an exemplary method 400, the function of determining theadvertisement value of a given advertising space may be accomplishedusing various techniques. In some instances, there may be a built-inassumption that the ad value for a given ad space is equal to whateveris being paid for the ad space by the advertiser. Accordingly, theserver system may simply determine a price that was paid for the adspace by an advertiser. For example, the server system may determine afixed price that was paid for the ad space, or may determine a pricerate (e.g., dollars per month, dollars per view, etc.).

However, in some cases, it may not be assumed that the ad value for anad space is equal what the advertiser paid; or in other words, someembodiments may allow for the possibility of an advertiser “getting morethan what they paid for.” For example, if each view by a certain type ofperson (e.g., the target market) is considered to be worth a certainamount to an advertiser, the advertiser may pay an amount based on theexpected number of views by the target market. However, if an ad spacereceives more than the expected number of views from the target market,then from the advertiser's perspective, the ad space is worth more thanthey paid for it. Furthermore, there may be cases where an ad space hasbeen valued or could be valued, but has not been purchased by anadvertiser.

Accordingly, the server system may additionally or alternatively use ameasure of advertisement value other than what was actually paid for theadvertisement space. For example, the server system may query anadvertisement-value database, which indicates advertisement values thatare based on detected occurrences of the advertisement space in gazedata from a plurality of wearable computing devices (and possibly gazedata from other types of devices as well). Methods for determining an advalue for and ad space based on gaze data from a number of wearablecomputing devices are described in greater detail with reference toFIGS. 5A to 7.

Further, in some embodiments, individual gaze valuation may beimplemented in conjunction with an ad marketplace where advertisementspaces are generally valued based on gaze data. In such an embodiment,the server system may also use as the ad value, the price at which an adspace is being offered for sale in the ad marketplace.

It should be understood that techniques described herein for determiningan ad value for a given ad space are not intended to be limiting. Othertechniques for determining an ad value based on the ad-valuecontributions of individual occurrences are also possible, withoutdeparting from the scope of the invention. Further, it should beunderstood that the technique and/or format of the ad value may varyfrom one occurrence to another within the gaze data associated with agiven account. For example, the server system may might use the actualpurchase price for an ad space, when the purchase price is available,but use the ad value based on gaze data when a purchase price is notavailable or is otherwise deemed to be less accurate (e.g., when thepurchase price is considered out of date).

In an exemplary method 400, the function of determining the portion ofthe advertisement value that is attributable to a given occurrence of agiven advertisement space may be accomplished using various techniques.

In some embodiments, the server system may attribute an equal portion ofthe advertisement value to each occurrence of a particular ad space. Forexample, the portion of an ad space's value that is attributed to agiven occurrence may be calculated by dividing the advertisement valueby a total number of occurrences detected in gaze data from a number ofdevices associated with a number of different user-accounts. Forinstance, if an advertisement space is valued at $100 per month and theadvertisement space averages ten occurrences per month in all availablegaze data, a $10 portion may be attributed to a given occurrence of thead space.

In some cases, the portion of an ad space's value that is attributed toa given occurrence may be based on an estimated number of total views ofthe ad space. In particular, the server system may extrapolate from theoccurrences that are detected to determine an estimated number of totalviews. This estimation may account for all views of an ad space,regardless of whether the view was captured in gaze data. This may beuseful as there may be many cases where those that view an ad space arenot wearing a wearable computer that is configured to capture and/orprovide gaze data. As one specific example, consider the case where itis assumed that one out of every thousand views will be captured in gazedata. In this case, if an advertisement space is valued at $100 permonth and the advertisement space averages ten occurrences per month inall available gaze data, the server system may calculate that there are10,000 views per month. Accordingly, a $0.01 portion may be attributedto a given occurrence of the ad space. Other examples are also possible.

In some embodiments, an exemplary method may help account for the factthat views of an ad space by certain people may be considered morevaluable than views of the same ad space by other people. As such,various types of information provided by and/or related to a givenuser-account may be used to determine an ad-value portion that isattributable an occurrence of an ad space in gaze data associated withthe given user-account. For instance, a user-account may include orprovide access to: (a) consumer information such as spending habits,locations of purchases, amounts of purchases, types or categories ofpurchases, timing of purchases, etc., (b) demographic information suchas age or age group, ethnicity, nationality, sex, location of residence,and/or location of workplace, (c) contact and/or social networkinginformation such as a user's contacts, and possibly data indicating apurchasing influence of the user with regard to their contacts (e.g.,data indicating any correlation of the user's purchasing history to thewearers' friends' purchasing histories), and/or (d) other informationsuch as income, job or job type, other job details, hobbies, interests,and so on. When a user has given permission for information from theiruser-account to be used for purposes of determining their individualgaze value, the server system may use such information to determine anad-value portion that is attributable to a given occurrence of an adspace in gaze data associated with the user's account.

It should be understood that many other types of information provided byand/or related to a given user-account may be considered, alone or incombination, when determining the portion of the ad value to attributeto an occurrence of an ad space in gaze data for the given user-account.Further, information provided by and/or related to a given user-accountmay be considered in combination with other factors, such as duration ofan occurrence and/or a focus value associated with the occurrence, whendetermining the portion of the ad value to attribute to the occurrence.

H. Gaze-Value Contributions on Per-Ad Basis

In the above examples, the gaze value is based upon ad-value portionsthat are determined on a per-occurrence basis. However, in someembodiments, the gaze value may only consider each ad space once whendetermining a gaze value for a given user-account. As such, the serversystem may effectively ignore subsequent occurrences of the same adspace in gaze data associated with a given user-account. For example,the server system may determine that a given ad space has occurred ingaze data for a certain number of user-accounts. The server system maythen divide the ad value for the ad space by this number to determinethe portion that is attributable to one user-account. As anotherexample, the server may extrapolate from the total number ofuser-accounts have provided gaze data including an occurrence of a givenad space to determine an estimated number of users that have viewed (orwill view) the ad space, and divide the ad value by the estimated numberof users who have viewed the ad space, in order to determine the portionof the ad value that is attributable to one user-account. Other examplesare also possible.

VI. Sending a Gaze-Value Indication

Referring back to method 100 of FIG. 1, once a gaze value for a givenuser-account has been determined, the server may send a gaze-valueindication to the given user-account, which indicates the individualgaze value that was determined. This may be accomplished in variousways. For example, the server system may (a) send an e-mail message toat least one e-mail account associated with the first user-profile, (b)send a text or multimedia message to at least one phone numberassociated with the first user-profile, (c) initiate an automated phonecall to at least one phone number associated with the firstuser-profile, and/or (d) display the determined gaze value in a webbrowser or another application via which the user has accessed theiruser-account. Other techniques for providing a user with theirindividual gaze value are also possible.

VII. Determining the Value of an Advertisement Space

As noted, in some embodiments, an exemplary system may also beconfigured to use gaze data to determine advertisement values for adspaces, in addition to using the gaze data to determine individual gazevalues for users. In such an embodiment, an exemplary system may beconfigured to determine an advertisement value for almost any time ofphysical space.

FIG. 5A is a flow chart illustrating a method according to an exemplaryembodiment. This method may be implemented by a computing device, and inparticular, by a server system, in order to value an advertisement spacebased on point-of-view gaze data received from a number of wearablecomputing devices (which may be referred to interchangeably as wearablecomputing devices). Note that wearable computing devices may also bereferred to as wearable computers herein. Further, a server system thatimplements an exemplary method may be referred to as an ad-valuationsystem, as an ad-valuation server, or simply as a server.

As shown by block 502, method 500 involves a server system receivinggaze data from a number of wearable computing devices. The server systemanalyzes the gaze data from the wearable computing devices to detectoccurrences of an advertisement space in the gaze data, as shown byblock 504. The server system then generates wearer-view data for theadvertisement space, which is based on detected occurrences of theadvertisement space in the gaze data, as shown by block 506. Thewearer-view data can then be used as a basis for determining anadvertisement value for the advertisement space, as shown by block 508.Once the advertisement value is determined, the server system may causea computing system to make the advertisement space available forpurchase at the determined advertisement value, as shown by block 510.

In an exemplary method 500, the gaze data is received from a number ofwearable computing devices. Further, the gaze data from each wearablecomputing device is generally indicative of a respective wearer-viewassociated with the given wearable computing device. For example, thegaze data from each wearable computing device may take the form ofpoint-of-view video that is captured at the wearable computing device.As such, the gaze data that is analyzed by the server system may includea number of point-of-view videos (e.g., a respective point-of-view videofrom each of the wearable computing devices).

The gaze data from some or all of the wearable computing devices thatprovide gaze data may additionally or alternatively take forms otherthan point-of-view video. For example, the gaze data from some or all ofthe wearable computing devices may take the form of respectivepoint-of-view images captured by a forward- or outward-facing camera onthe respective wearable computing device. As a specific example, a givenwearable computing device may periodically take a picture, and then sendthe picture to the server system for use in generating wearer view data.To do so, the wearable computing device may analyze point-of-view videofor one or more ad spaces, and generate a screen capture of the videowhen and ad space detected. The wearable computing device may then sendthe screen capture to the server system. Other examples are alsopossible.

Since the gaze data from a given wearable computing device is generallyindicative of the wearer-view of the wearable computing device's wearer,the gaze data is generally indicative of what the wearer of the deviceis actually looking at. Further, since the wearer-view data is based onthe gaze data, the wearer-view data is indicative of actual views of thead space by wearers. For instance, the wearer-view data may provide anindication of how many people are looking at a particular advertisementspace, which people are actually looking at a particular ad space, whenpeople are looking at a particular ad space, and/or how long people areactually looking at a particular ad space, among other information. Assuch, the wearer-view data may help to more accurately determine what anadvertising space is worth.

As noted above, when occurrences of an ad space are detected in gazedata, an exemplary method 500 may involve generating wearer-view datathat is based on the detected occurrences. As such, an exemplary serversystem 204 may be configured to carry out an exemplary method 500 orportions thereof for many different advertisement spaces. Generally, theaccuracy of the ad-space valuation will typically increase as the numberof wearable computing devices providing gaze data increases. However,the gaze data may be collected from any number of wearable computingdevices without departing from the scope of the invention.

To facilitate determining an advertisement value for a given ad space,the wearer-view data may provide various types of information. Forexample, the wearer-view data for a given ad space may include, for eachdetected occurrence of the given ad space: (a) data indicating theparticular wearable computing device that provided the gaze data inwhich the ad space occurred, (b) data indicating a user-profileassociated with the particular wearable computing device, (c) dataindicating a time of the detected occurrence, (d) a duration of thedetected occurrence, and/or (e) other information.

Generally, the function of generating wearer-view data for theadvertisement space, as shown in block 506 of method 500, may varydepending upon the information to be included in the wearer-view data.In an exemplary embodiment, detecting an occurrence of an advertisingspace in the gaze data may serve as a trigger for the server system togenerate wearer-view data recording the fact that the occurrence wasdetected. Further, to generate the wearer-view data for a givenoccurrence, the server system may extract information from the gaze datain which the occurrence was detected. The extracted information (orinformation derived from the extracted information) may be included inthe wearer-view data generated for the detected occurrence.

A. Per-Occurrence Data for an Ad Space

In some embodiments, the server system 204 may update the wearer-viewdatabase 308 upon each detected occurrence of an ad space. For example,the server system may generate a record in the wearer-view database foreach detected occurrence of an ad space. In such an embodiment, therecord for a given occurrence of an ad space may include: (a) anindication of the particular wearable computing device that provided thegaze data in which the ad space occurred, (b) an indication of auser-profile associated with the particular wearable computing device,(c) a time of the occurrence, and/or (d) a duration of the occurrence.

The wearer-view data for a given occurrence of an ad space may indicatethe corresponding wearable computing device that provided the gaze datain which the ad space occurred. In such an embodiment, the server systemmay determine the corresponding wearable computing device in variousways. For instance, consider an embodiment where the server systemreceives point-of-view (POV) video stream from a number of wearablecomputing devices. In such an embodiment, the server system mayestablish a communication session to receive the video stream from agiven one of the wearable computing devices, and as part of establishingand/or participating in the session, may receive an identifier of thewearable computing device. (Note that various protocols, which are wellknown in the art, may be used to receive a POV video stream and/or toreceive other forms of gaze data.) Additionally or alternatively,metadata in the gaze data itself may include an identifier of thewearable computing device that is providing the gaze data. Othertechniques for determining which wearable computing device correspondsto a particular occurrence of an ad space are also possible.

As further noted above, the wearer-view data for a given occurrence ofan ad space may indicate an associated user-profile, which is associatedwith the wearable computing device that provided the gaze data havingthe particular occurrence. The server system may determine theassociated user-profile in various ways. For example, the server maydetermine the identifier for the corresponding wearable computing devicein a manner such as described above or otherwise. The server may thenlook up a user-profile of a user that is registered to use or isotherwise associated with the corresponding wearable computing device(e.g., by querying a user database that indicates which users areassociated with which wearable computing devices). Alternatively, auser-identifier may be provided in the course of receiving the gaze data(e.g., in a communication session or in metadata). In such anembodiment, the server system may use the user-identifier to access auser-profile for the user. As another alternative, the user-profileitself may be received directly from the device (e.g., during thecommunication session in which the gaze data is received, as metadataincluded in the gaze data, or in a separate message that is associatedwith the gaze data). Other techniques for determining a correspondinguser-profile for a particular occurrence of an ad space are alsopossible.

In a further aspect, when the wearer-view data for a given occurrenceindicates the associated user-profile, the wearer-view data may simplyinclude an identifier of the associated user-profile. In such anembodiment, the data from such user-profiles may be stored in one ormore separate user-profile databases. In this case, the server may usethe identifiers of the associated user-profiles to retrieve the datafrom the actual user-profiles. Alternatively, some or all of the datafrom the associated user-profile may be included in the wearer-view datafor the ad space (e.g., in wearer-view database 308).

In a further aspect, the server system may include a time stamp in thewearer-view data that is generated for a given occurrence. The timestampmay indicate the time at which the occurrence of the ad space wasdetected. Additionally or alternatively, the timestamp may indicate atime that is derived from time data included in the gaze data. Forexample, point-of-view video from a given wearable computing device mayinclude time data indicating when the video was recorded by the wearablecomputing device. As such, the server system may use this time data togenerate a timestamp for an occurrence that is detected in suchpoint-of-view video. For instance, the server system may determine aframe or frames of the video that include the ad space, and use a timestamp or time stamps of the frame or frames to generate the timestampfor the detected occurrence. Other techniques for generating a timestampfor a particular occurrence of an ad space are also possible.

In another aspect, the wearer-view data for a given occurrence of an adspace may indicate the duration of the given occurrence. Accordingly,the server system may be configured to determine the duration of a givenoccurrence of an ad space. For instance, in the above example where POVvideo includes time data, the server system may use timestamps on framesof the video to determine the duration of time the first frame of thevideo that includes the ad space and the last subsequent and consecutiveframe that includes the ad space. Alternatively, the server system mayimplement its own timer to determine the duration of a given occurrenceof an ad space. Other techniques for determining the duration of aparticular occurrence of an ad space are also possible.

In a further aspect, when generating wearer-view data for a givenoccurrence, the server may consider whether the wearable computingdevice that corresponds to a given occurrence was being worn during theoccurrence. In particular, if the corresponding wearable computingdevice is not being worn at the time of the detected occurrence, theserver may adjust or change the wearer-view data that is generated inresponse to detecting the occurrence. For example, when the wearablecomputing device is not being worn, the server may interpret this tomean that the gaze data from the wearable computing device is unlikelyto represent what the wearer is actually viewing. Accordingly, theserver may include an indication that the wearable computing device wasnot being worn in the wearer-view data that is created for such anoccurrence. Further, server may adjust the wearer-view data so as todecrease the weight of such an occurrence when determining the ad valuefor the ad space, or may ignore the occurrence entirely (e.g., byrefraining from generating any wearer-view data for the occurrence).

B. Summary Data for an Ad Space

In some embodiments, the wearer-view data for a given ad space mayinclude summary data for the ad space such as: (a) a list of whichwearable computing devices viewed the ad space (e.g., which wearablecomputing devices provided gaze data in which one or more occurrenceswere detected), (b) a list of the user-accounts or the user-profilesthat are associated with the wearable computing devices that have viewedthe ad space, (c) a total view count indicating the total number ofdetected occurrences of the ad space, (d) a total view duration of thead space, (e) an average view duration for occurrences of the ad space,and/or (f) a view rate that indicates how frequently the advertisementspace occurs in the gaze data (e.g., occurrences/hour,occurrences/month, etc.). The wearer-view data for a given ad space mayadditionally or alternatively include other types of summary data forthe ad space.

In order to keep the above and other such summary data substantiallycurrent, the server system may update the wearer-view data for an adspace each time the ad space is detected in gaze data. For example, whenthe server system detects an ad space in gaze data from a given wearablecomputing device, the server system may update the wearer-view data by:(a) adding the given wearable computing device to a list of wearablecomputing devices that have viewed the ad space (if the wearablecomputing device is not on the list already), (b) adding theuser-account or the user-profile that is associated with the givenwearable computing device to a list of user-accounts or user-profilesthat have viewed the ad space, (c) incrementing the total view count forthe ad space, (d) determining the duration of the occurrence and addingthe determined duration to a total view duration for the ad space,and/or (e) determining the duration of the occurrence and adding thedetermined duration and recalculating the average view duration toaccount for the determined duration. Other examples are possible aswell.

In some embodiments, the wearer-view data for each ad space may includeonly summary data such as that described above, and thus may not includeper-occurrence data for each detected occurrence of an ad space.However, it is also possible that the wearer-view data for a given adspace may include only per-occurrence data, or may include bothper-occurrence data and summary data for the ad space.

C. Focus Data for an Occurrence

In some embodiments, the wearer-view data for a given ad space mayinclude focus data, which is generally indicative of the amount ofattention paid to an ad space by viewers of the ad space. The focus datamay help to provide a more accurate valuation for the ad space byhelping take into account the fact that not all views are necessarilyequal, since the amount of attention paid to the ad space may varybetween views. In such an embodiment, the server system may determine afocus value for a detected occurrence (as described above) when itgenerates wearer-view data for the occurrence, or may determine a focusvalue at a later time.

D. Use of Summary Data for Advertisement Valuation

As noted above, an exemplary method 500 may involve using thewearer-view data for an ad space to determine an advertisement value forthe advertisement space. Various types of wearer-view data may beutilized when determining an advertisement value. For instance, varioustypes of the summary data described above and/or various types of theper-occurrence data described above may be used to determine theadvertisement value for a given ad space. An exemplary valuation methodmay also incorporate other types of data in addition to wearer-viewdata. Further, the manner in which a given type of wearer-view data isused to determine an advertisement value may vary depending upon theimplementation.

In some embodiments, the ad value for a given ad space may be based onsummary data for the ad space. For example, the ad value may be based atleast in part on the total view count for an ad space (e.g., the totalnumber of occurrences that are detected in the gaze data). In such anembodiment, the total number of occurrences may be tracked over alltime. Alternatively, the total number of occurrences may be tracked overa predetermined period of time (e.g., a year, a month, a week, or acustom-defined time period). In an exemplary embodiment thatincorporates total view count, the determined advertisement value willtypically increase as the total number of occurrences increases.Further, the manner in which the total view count is used to determinead value may vary, depending upon the implementation.

As another example, the ad value for a given ad space may be based atleast in part on a view rate for the advertisement space (e.g., the rateat which occurrences of the ad space are detected in the gaze data). Forinstance, the wearer-view data may indicate a number of views per month,per week, per day, per hour, etc. In such an embodiment, the rate may bebased on detected occurrences over all time. Alternatively, the rate maybe based on occurrences during a predetermined period of time (e.g.,during a year, a month, a week, or a custom-defined time period). In anexemplary embodiment that incorporates view rate, the determinedadvertisement value will typically increase as the view rate increases.Further, the manner in which the view rate is used to determine ad valuemay vary, depending upon the implementation.

In the above examples, the ad value is determined based on summary datathat generally does not differentiate one detected occurrence fromanother. However, some embodiments may apply further intelligence toaccount for the fact that some views of an ad space may be more valuableto an advertiser than others.

For example, the ad value for a given ad space may be based at least inpart on a total view duration and/or an average view duration for the adspace. In such an embodiment, the total view duration and/or the averageview duration may be calculated from all detected occurrences of the adspace or from a representative sample of occurrences. In either case,the total view duration and/or the average view duration may becalculated over all time, or may be calculated over a predeterminedperiod of time (e.g., a year, a month, a week, or a custom-defined timeperiod). In an exemplary embodiment that incorporates total viewduration and/or the average view duration, the determined advertisementvalue will typically increase as the total view duration and/or theaverage view duration increases. Accordingly, views that last longerwill generally contribute more to the ad value and/or be weighted moreheavily when determining the ad value. It should be understood that themanner in which the total view duration and/or the average view durationis used to determine ad value may vary, depending upon theimplementation.

As another example, the server may determine focus values for all or arepresentative sample of the detected occurrences of an ad space. Theserver may then average the focus values for the detected occurrences todetermine an average focus value for the ad space. The server can thenuse the average focus value to determine the ad value for the ad space.

In a further aspect, an exemplary embodiment may help account for thefact that views of an ad space by certain people may be considered morevaluable than views of the same ad space by other people. Morespecifically, in an exemplary embodiment, wearers may opt-in to aprogram or otherwise give permission for information from theiruser-profile to be used to value ad spaces. Various types of informationfrom an associated user-profile may then be used to determine howvaluable a given occurrence of an ad space is. For instance, auser-profile for a wearer may include: (a) consumer information such asspending habits, locations of purchases, amounts of purchases, types orcategories of purchases, timing of purchases, etc., (b) demographicinformation such as age or age group, ethnicity, nationality, sex,location of residence, and/or location of workplace, (c) contact and/orsocial networking information such as a wearer's contacts, and possiblydata indicating a purchasing influence of the wearer with regard totheir contacts (e.g., data indicating any correlation of the wearer'spurchasing history to the wearers' friends' purchasing histories),and/or (d) other information such as income, job or job type, other jobdetails, hobbies, interests, and so on.

Therefore, since the occurrence of an ad space in gaze data from a givenwearable computing device may be interpreted to mean that the wearer ofthe given wearable computing device has viewed or is viewing the adspace, information from user-profiles that wearer-view data associateswith a given ad space may provide information about the type or types ofpeople that an ad space reaches and/or of the characteristics of peoplethat the ad space reaches. As a result, this information may be used tomore accurately determine what types of people are viewing the ad space,and value the ad space accordingly. In particular, an exemplary servermay place greater weight on occurrence of an ad space associated withcertain people and/or certain types of people when determining the advalue for a given ad space.

For example, the server may determine a respective income level for theuser-profile associated with each occurrence. The server may thenaverage the determined income levels to calculate an average incomelevel for viewers of the ad space, and use the average income level asinput data to determine the ad value for the ad space. Alternatively,the server may determine an income range of the determined incomelevels, and use the income range as an input to the ad-value calculationfor the ad space. Other examples are also possible.

It should be understood that the ad value for a given ad space may bebased upon one type of summary data or a combination of various types ofsummary data. For example, in one implementation, the total number ofviews, the view rate, the average view duration, and one or morecharacteristics of the associated user-profiles, could all be used asinputs when calculating ad value. Many other examples are also possible.

E. Use of Per-Occurrence Ad-Value Contributions for AdvertisementValuation

In some embodiments, a server system may determine an advertisementvalue for an ad space by first determining an individualadvertisement-value contribution for each detected occurrence of theadvertisement space. The advertisement-value contribution for a givenoccurrence may be based on information from the user-profile associatedwith the occurrence and/or on other information related to theoccurrence. The collective knowledge provided by all the individualadvertisement-value contributions may then be used to determine theadvertisement value for the advertisement space and/or be used todetermine summary data for the ad space, which may in turn be used todetermine the ad value.

FIG. 5B is a flow chart illustrating a method for determiningadvertisement value, according to an exemplary embodiment. Inparticular, FIG. 5B illustrates a method 550 in which the advertisementvalue for an ad space is based on individual ad-value contributions ofoccurrences of the ad space in gaze data.

More specifically, method 550 involves monitoring gaze data from anumber of wearable computing devices for occurrences of a particularadvertisement space, as shown by block 552. Each time an occurrence ofthe advertisement space is detected, as shown by block 554, the serversystem may further determine an advertising-value contribution of theoccurrence, as shown by block 556. The server system may then determineadvertising-value contributions for a number of occurrences by repeatingblocks 554 and 556 for a number of detected occurrences of theadvertisement space. The server system may then use theadvertising-value contributions for the detected occurrences of theadvertisement space as a basis for determining the advertisement valuefor the advertisement space, as shown by block 558.

In some embodiments, such as method 550 of FIG. 5B, the ad-valuecontribution for each occurrence of an ad space may be determined upondetecting the occurrence in the gaze data. However, it should beunderstood that the ad-value contribution for some or all occurrences ofan ad space may be calculated at a later time, based on wearer-view datathat is stored as the occurrences are detected in the gaze data.

A server may use various techniques to determine the individual ad-valuecontribution for a given occurrence of an ad space in gaze data. Suchtechniques may utilize various different factors to determine thead-value contribution for a given occurrence. In some embodiments, theserver may use a weighting value for an occurrence to determine thead-value contribution of the occurrence. In particular, the server maydetermine a weighting value that generally indicates the particularoccurrence's value relative to a “standard” occurrence of the ad space.The weighting value for the particular occurrence may then be applied toa base advertising-value contribution to determine the advertising-valuecontribution for the particular occurrence. In such an embodiment, theweighting value may be based on various factors or combinations offactors, such as the particular wearable computing device from which thegaze data including the particular occurrence was received, the durationof the occurrence, characteristics of the person who viewed (e.g., asindicated by the user-profile associated with the occurrence), the focusvalue of the occurrence, and/or other factors.

As a specific example, the ad-value contribution for each occurrence ofthe ad space may be a dollar amount that is attributed to theoccurrence. As such, the server may determine a dollar amount for the advalue by summing the ad-value contributions. As another example, thead-value contribution for each occurrence of the ad space may be a pricerate (e.g., dollars per month, dollars per view, etc.) that isattributed to a respective occurrence of the ad space. As such, theserver may determine the ad value by summing the ad-value contributionsto get an overall price rate. Other examples are also possible.

Once a server system has determined the individual ad-valuecontributions for a number of occurrences of the particular ad space,the server may use various techniques to determine the ad value for thead space. For example, in some embodiments, the server may determine anaverage advertising-value contribution by averaging theadvertising-value contributions of some or all of the detectedoccurrences. The server may then use the average advertising-valuecontribution as a basis for determining the advertisement value for thead space. As a specific example, the server may determine an ad-valuecontribution for each occurrence in the same manner as the overall priceor rate for the ad space, but using the assumption that all occurrencesof the ad space are identical to the occurrence. The server may thendetermine a dollar amount or price rate for the ad space by averagingthe ad-value contributions determined in this manner. Other examples arealso possible.

It should be understood that techniques described herein for determiningan ad value based on the ad-value contributions are not intended to belimiting. Other techniques for determining an ad value based on thead-value contributions of individual occurrences are also possible,without departing from the scope of the invention.

F. Valuation of an Ad Space on a Per-Advertisement Basis

Some embodiments may involve determining a value an ad space that isspecific to a particular type of advertisement. For example, an adserver may determine a value for an ad space when the ad space is usedto display an ad for a particular type of product (e.g., for clothing orfor a movie).

Further, in some embodiments, an exemplary method may be implemented forad-specific valuation of an ad space based on the extent to which the adspace reaches the target market of the advertisement. For instance,wearer-view data may be used to determine who is viewing an ad space.The ad space valuation may then be based on the correlation betweenthose who view the ad space and the target market of the specificadvertisement.

In such an embodiment, an exemplary method may utilize wearer-view dataindicating user-profiles associated with occurrences of an ad space inthe gaze data. As such, the server may analyze the associateduser-profiles to determine one or more characteristics of those who haveviewed the ad space. More specifically, an exemplary method may involvedetermining a group of user-profiles associated with the advertisementspace (e.g., user-profiles that are associated with wearable computingdevices that captured the ad space in their respective gaze data). Then,based on characteristics of the associated user-profiles, the server maydetermine one or more viewer characteristics of the group. The viewercharacteristics of the group may be interpreted as indicative of a“standard” viewer of the ad space. As such, the viewer characteristicsof the group may incorporate when determining the advertisement valuefor a specific type advertisement.

For example, some embodiments may involve determining both: (a) theviewer characteristics of the group of associated user-profiles and (b)one or more target-viewer characteristics for the particularadvertisement. The server may then compare the viewer characteristics ofthe group to the target-viewer characteristics and, based on thecomparison, determine the advertisement value for the particularadvertisement in the advertisement space.

In some embodiments, the ad value for the particular advertisement maybe further based on the location of the ad space. In particular, theremay be a relationship between the characteristics of a particularadvertisement and the location of ad space, and an exemplary method mayhelp to account for such a relationship. In such cases, a weightingfactor may be applied to increase or decrease the ad value dependingupon the relationship between the location of the ad space and thecharacteristics of the advertisement.

For example, consider an advertisement for a clothing product and an adspace that is located near to a shopping area and/or near to a storewhere the clothing product can be purchased. This ad space may generallybe considered more valuable when used to display the advertisement forthe clothing product than when used to display an ad for a type ofproduct that cannot be purchased nearby. Accordingly, a weighting factormay be applied to increase the ad value for the clothing product in thead space. Similarly, the weighting factor may function to decrease thead value for a product that cannot be purchased nearby.

As another example, consider an advertisement for a movie and an adspace that is located near to a movie theater that is showing the movie.This ad space may generally be considered more valuable when used todisplay the advertisement for the movie than when used to display an adfor a movie that is not in any nearby theaters. Accordingly, a weightingfactor may be applied to increase the ad value of the ad space for amovie that is showing in the nearby theater. Similarly, the weightingfactor may decrease the ad value for the movie that is not in any nearbytheaters. Other examples are also possible.

In a further aspect, some implementations of method 500 may utilize thetype of advertisement as the characteristic of the advertisement uponwhich the ad value is based. In such an embodiment, all advertisementsof the same type may be evaluated in the same way. As such, the ad valuein such an embodiment may in effect be determined for the type ofadvertisement in the ad space (rather than specifically for anindividual advertisement). Alternatively, the type of advertisement maybe one of a number of factors, such that an ad space may be valueddifferently for different advertisements that are classified as beingthe same type of advertisement.

G. Adjusting the Ad Value Based on Other Factors

In some embodiments, ad valuation may be based on other types of data,in addition to wearer-view data. In such an embodiment, the ad servermay determine a base value for the advertisement, or a weighting to beapplied to the ad value based on an intrinsic value of the ad space,which accounts for the characteristics of the ad space itself, and thenadjust the intrinsic value according to the wearer-view data.

For example, in some embodiments, an exemplary method may use thegeographic location of the advertisement space as a further basis fordetermining the advertisement value for the advertisement space. Forexample, an advertisement that is located in a shopping area may have agreater intrinsic value than one that is located in an alley.Accordingly, an ad value that is determined based on wearer-view datamay be adjusted based on the location of the ad space. Other examplesare also possible. Generally, the type and/or the amount of adjustmentthat is applied due to the location of an ad space may vary dependingupon the particular implementation.

Further, in some embodiments, the server may consider the type ofadvertisement space and/or the format in which advertisements can bedisplayed in the ad space when determining the advertisement value forthe advertisement space. For example, an LCD billboard might generallybe considered more valuable than an equivalent print billboard. As such,when an ad value is determined for a billboard based on wearer-viewdata, the determined ad value may be adjusted based on whether thebillboard is an LCD or a print billboard. Other examples are alsopossible. Generally, the type and/or the amount of adjustment that isapplied based on the type of advertisement space may vary depending uponthe particular implementation. Further, other adjustments, based onother characteristics of an ad space, are also possible.

In another aspect, an ad space may be blank (e.g., not displaying anadvertisement) during some or all of the period in which gaze data isbeing collected for purposes of determining the ad value. The fact thatan ad space is blank, as opposed to displaying an advertisement, mayaffect the gaze data for the ad space because a blank space mightattract less attention from nearby people. Further, differentadvertisements may attract different amounts of attention. Therefore,when an advertisement is displayed while gaze data is being collected,the particular advertisement may itself affect the gaze data. As such,it is possible that wearer-view data for an ad space may be effected bywhether or not an ad space is blank, and if something is displayed inthe ad space, what specifically is displayed.

Accordingly, an exemplary method may further involve determining apre-sale weighting factor for an advertisement space, which is based on:(a) whether the ad space is blank while gaze data is being collectedand/or (b) the characteristics of what is displayed in the ad spacewhile gaze data is being collected. A server may then use the pre-saleweighting factor for the ad space as a further basis for determining theadvertisement value for the advertisement space.

As a specific example, an exemplary method may further the serverdetermining whether or not the advertisement space had an advertisementin place while receiving and analyzing the gaze data. Then, if theadvertisement space had an advertisement in place, the server may applya first adjustment to the wearer-view data before using the wearer-viewdata to determine the advertisement value (e.g., an adjustment orweighting factor that corresponds to the particular advertisement thatis displayed). On the other hand, if an advertisement was not displayedin the advertisement space, then the server may apply a secondadjustment to the wearer-view data (e.g., an adjustment that accountsfor the fact that no advertisement was displayed).

In such an embodiment, the server may determine whether or not theadvertisement space had an advertisement in place in various ways. Forexample, the server may query an ad space database to determine whetherthe ad space is in use and if so, what advertisement is being displayed.Additionally or alternatively, the server may analyze the gaze dataitself (e.g., by analyzing point-of-view video in which the ad space isdetected). Other examples are also possible.

In yet another aspect, some embodiments may implement gaze-datarequirements that require a certain amount of gaze data be analyzedbefore an ad space can be offered for sale in an ad-marketplace system.For instance, an ad-marketplace system may require that gaze data from athreshold number of devices have been analyzed before the determined advalue is deemed accurate enough to offer the ad space for sale via themarketplace. Additionally or alternatively, ad-marketplace system mayrequire that gaze data be monitored for a certain period of time (e.g.,at least a week) before the determined ad value is deemed accurateenough to offer the ad space for sale.

Further, in some embodiments, wearer-view data requirements may requirethat a certain amount of wearer-view data be generated before an adspace can be offered for sale in an ad-marketplace system. For example,an ad-marketplace system may require that a certain number ofoccurrences of an ad space be detected before the determined ad value isdeemed accurate enough to offer the ad space for sale via themarketplace (or in other words, require that the wearer-view data takesinto account at least a threshold number of occurrences). Other examplesare also possible.

VIII. Use of Supplemental Gaze Data from Non-Wearable Computing Devices

In some embodiments, an exemplary method may incorporate supplementalgaze data from one or more non-wearable computing devices. In such anembodiment, the supplemental gaze data may include media captured by thenon-wearable computing devices. For example, supplemental gaze data maybe received from mobile phones, tablet computers, network-enabled videoand/or still cameras, and/or other non-wearable computing devices.

Similar to the gaze data from wearable computing devices, thesupplemental gaze data is generally indicative of a respective user-viewassociated with a device that provides supplemental gaze data.Accordingly, an exemplary method may further involve receivingsupplemental gaze data from one or more non-wearable computing devicesthat are registered to a given user-account. A server may then detectadditional occurrences of advertisement spaces in the supplemental gazedata, and factor the additional occurrences in when determining the gazevalue for the user-account.

However, because supplemental gaze data is captured at non-wearabledevices, supplemental gaze data may less reliably represent what theuser actually sees, as compared to gaze data captured by a wearabledevice that is physically worn on the user's person. Accordingly, in anexemplary method, the server may weight supplemental occurrences thatare detected in the supplemental gaze data in order to account for theincreased probability that the supplemental gaze data does not representwhat the user actually sees. For example, the server may weight asupplemental occurrence by a significance factor corresponding to thelikelihood that the corresponding supplemental gaze data is indicativeof a respective user-view associated with the non-wearable computingdevice that provided the supplemental gaze data in which thesupplemental occurrence was detected.

In a further aspect, systems may be implemented to actively searchsupplemental gaze data that is pre-recorded, such as image librariesthat are accessible via a network. For example, a server or anassociated system may be configured to analyze images from one or moreonline image albums to determine supplemental user-view data for theadvertisement space. In such an embodiment, the supplemental user-viewdata is based on occurrences of the advertisement space in the pluralityof images.

For example, the system may search image albums on a photo-sharingwebsite, social-network website, or another network source, foroccurrences of the ad space in the images. When an occurrence is found,the system may generate supplemental user-view data for the occurrence.For instance, many such websites require that users open a user-accountin order to create photo albums and/or share photos. Accordingly, thesystem may store data linking the occurrence of the ad space to theuser-account via which the image was shared.

In a further aspect, one image of an ad space may be indicative of amore valuable view of the ad space than another image. As such, eachimage that includes the ad space may be evaluated for indications of howsignificant the occurrence is, so that the occurrence may be weightedaccordingly when determining the ad value.

For example, an exemplary method may involve analyzing one or moreimages from one or more image albums to detect any occurrences of theadvertisement space in the images. Then, for each image where anadvertisement space is detected, the system may determine a gaze-valuecontribution and/or an advertising-value contribution for the givenimage (note that in some instances, the advertising-value contributionmay be used as the gaze-value contribution). As a specific example,determining a prominence value corresponding to a prominence of theadvertisement space in the given image (e.g., a size and/or location ofthe ad space in the image), and then use the prominence value as a basisfor determining a gaze-value contribution and/or an advertising-valuecontribution for the given image. The system may then use any gaze-valuecontributions from these images, such as any prominence values that aredetermined for any of the images, when determining the gaze value forthe associated user-account. Similarly, the system may use anyadvertising-value contributions from these images, such as anyprominence values that are determined for any of the images, whendetermining the advertisement value for the advertisement space.

In a further aspect, data from GPS systems and other sensors, such asmagnetometers, accelerometers, and/or gyroscopes may providesupplemental gaze data. In some embodiments, GPS on a wearable computingdevice or another device (e.g., a mobile phone or tablet) may provide anindication of location, and a magnetometer on the same device mayprovide an indication of orientation, such that it may be inferred thata user of the device is in a certain location and is facing a certaindirection. Further, the location of an ad space may also be determinedas described herein. Thus, if the user is inferred to be facing alocation where and space is located, this may be considered a view ofthe ad space, and thus may be factored into the wearer-view data. Otherexamples of using GPS and/or sensor data to infer supplemental gaze dataare also possible.

IX. Wearable-Computer-Based Functionality

The above-described methods and systems are generally described withreference to examples where wearable computing devices collect and sendgaze data to an ad-valuation server system, such that the server systemprovides most of the functionality as far as detecting ad spaces in gazedata, determining individual gaze values, and/or determining ad valuesfor ad spaces. This arrangement may be referred to as a “cloud-based”embodiment. However, it should be understood thatwearable-computer-based embodiments and partially cloud-basedembodiments are also possible. Thus, it should be understood that someor all of the functionality that is described herein as being carriedout by a server may alternatively be carried out at one or more wearablecomputing devices.

For example, FIG. 6 is a flow chart illustrating a method that may becarried out at a wearable computing device, according to an exemplaryembodiment. As shown by block 602, method 600 involves a wearablecomputing device receiving gaze data that is indicative of a wearer-viewassociated with the wearable computing device. At block 604, thewearable computing device analyzes the gaze data to detect occurrencesof advertisement spaces in the gaze data. Based at least in part on thedetected advertisement-space occurrences, the wearable computing devicemay determine an individual gaze value for a user-account that isassociated with the wearable computing device, as shown by block 606.

The wearable computing device may then send the individual gaze valuefor display, as shown by block 608. For example, the wearable computingdevice may simply send the gaze value for display in its own display(e.g., in an HMD), or may send the gaze value to another computingdevice of the wearer (e.g., a mobile phone, tablet, or laptop computer).Other examples are also possible.

The function of analyzing gaze data for occurrences of advertisementspaces may be accomplished in various ways. In some embodiments, thewearable computing device may monitor gaze data as it is generated, sothat occurrences of ad spaces can be detected in real-time. In otherembodiments, the wearable computing device may store gaze data and thenlater search for occurrences of advertisement spaces in the gaze data.In yet other embodiments, a wearable computing device may implement acombination of real-time analysis and after-the-fact analysis of storedgaze data. For instance, the wearable computing device may search forsome ad spaces in real-time, as well as storing some or all gaze data sothat, if needed, the wearable computing device can search for other adspaces at a later time. Other techniques are also possible.

In a further aspect, the processing and/or storage capabilities ofindividual wearable computing devices may be limited as compared to aserver system. As such, when a wearable computing device stores gazedata for analysis at a later time, the wearable computing device maytake various actions to reduce the size of the gaze data before storingthe gaze data. For instance, rather than store full-size point-of-viewvideo that is captured at the wearable computing device, the wearablecomputing device may periodically generate and store screenshots fromthe point-of-view video. Alternatively, the wearable computing devicemay apply compression, and/or may reduce the resolution and/orframe-rate of the POV video, before storing the video. As anotheralternative, the wearable computing device may implement real-timeanalysis of the gaze data for occurrences of ad spaces, and also sendthe gaze data to the server to be stored in the event further analysisis needed at a later time.

In a variation on method 600, the wearable computing device may notifythe server each time it detects an occurrence of ad space in its gazedata, so that the server can use the detected occurrence whendetermining a gaze value for the user-account associated with thewearable computing device. In such an embodiment, the server maydetermine a gaze-value contribution of each occurrence that is indicatedby the wearable computing device, and use all or a subset of thesegaze-value contributions to determine the individual gaze value for theuser-account.

In a further aspect, when a wearable computing device detects an adspace in its gaze data, the wearable computing device may determinewhether it is being worn when the ad space is detected. The wearablecomputing device may then adjust or change the manner in which such anoccurrence of an ad space is used to determine the individual gazevalue, in the event the wearable computing device is not being worn atthe time of the detected occurrence. For example, if the wearablecomputing device is not being worn when an ad space is detected, thismay be interpreted to mean that the gaze data that included the ad spaceis unlikely to represent what the wearer is actually viewing.Accordingly, the wearable computing device may reduce the significanceof the occurrence when determining the individual gaze value for theoccurrence, or may ignore the occurrence entirely (e.g., by refrainingfrom generating any wearer-view data based on the occurrence).Alternatively, in an embodiment where the wearable computing devicenotifies the server system of detected occurrences, the wearablecomputing device may notify the server system that the wearablecomputing device was not being worn when the particular occurrence wasdetected, or may simply refrain from notifying the server of ad-spaceoccurrence while the wearable computing device is not being worn.

Further, in embodiments where the wearable computing device notifies theserver of detected ad-space occurrences, the wearable computing devicemay notify the server when the wearable computing device is not beingworn during an occurrence. Alternatively, in such an embodiment, thewearable computing device may simply refrain from notifying the serverabout the particular occurrence and/or may refrain from sendingwearer-view data to the server that is based on the particularoccurrence.

Yet further, the wearable computing device may use various techniques todetermine whether or not the wearable computing device is being worn.For example, the wearable computing device may use various sensors, suchas accelerometers, gyroscopes, and/or compasses, to determine whetherthe position and/or motions of the wearable computing device arecharacteristic of the wearable computing device being worn. Additionallyor alternatively, the wearable computing device may use various sensorsto determine whether the wearable computing device is in contact with awearer's skin or positioned on the wearer's face. More generally, thewearable computing device may use any technique that is now known orlater developed to determine whether the wearable computing device isbeing worn.

X. Exemplary Wearable Computing Systems

FIG. 7A illustrates a wearable computing system according to anexemplary embodiment. In FIG. 7A, the wearable computing system takesthe form of a head-mounted device (HMD) 702 (which may also be referredto as a head-mounted display or a heads-up display (HUD)). It should beunderstood, however, that exemplary systems and devices may take theform of or be implemented within or in association with other types ofdevices, without departing from the scope of the invention. Asillustrated in FIG. 7A, the head-mounted device 702 comprises frameelements including lens-frames 704, 706 and a center frame support 708,lens elements 710, 712, and extending side-arms 714, 716. The centerframe support 708 and the extending side-arms 714, 716 are configured tosecure the head-mounted device 702 to a user's face via a user's noseand ears, respectively.

Each of the frame elements 704, 706, and 708 and the extending side-arms714, 716 may be formed of a solid structure of plastic and/or metal, ormay be formed of a hollow structure of similar material so as to allowwiring and component interconnects to be internally routed through thehead-mounted device 702. Other materials may be possible as well.

One or more of each of the lens elements 710, 712 may be formed of anymaterial that can suitably display a projected image or graphic. Each ofthe lens elements 710, 712 may also be sufficiently transparent to allowa user to see through the lens element. Combining these two features ofthe lens elements may facilitate an augmented reality or heads-updisplay where the projected image or graphic is superimposed over areal-world view as perceived by the user through the lens elements.

The extending side-arms 714, 716 may each be projections that extendaway from the lens-frames 704, 706, respectively, and may be positionedbehind a user's ears to secure the head-mounted device 702 to the user.The extending side-arms 714, 716 may further secure the head-mounteddevice 702 to the user by extending around a rear portion of the user'shead. Additionally or alternatively, for example, the HMD 702 mayconnect to or be affixed within a head-mounted helmet structure. Otherpossibilities exist as well.

The HMD 702 may also include an on-board computing system 718, a videocamera 720, a sensor 722, and a finger-operable touch pad 724. Theon-board computing system 718 is shown to be positioned on the extendingside-arm 714 of the head-mounted device 702; however, the on-boardcomputing system 718 may be provided on other parts of the head-mounteddevice 702 or may be positioned remote from the head-mounted device 702(e.g., the on-board computing system 718 could be wire- orwirelessly-connected to the head-mounted device 702). The on-boardcomputing system 718 may include a processor and memory, for example.The on-board computing system 718 may be configured to receive andanalyze data from the video camera 720 and the finger-operable touch pad724 (and possibly from other sensory devices, user interfaces, or both)and generate images for output by the lens elements 710 and 712.

The video camera 720 is shown positioned on the extending side-arm 714of the head-mounted device 702; however, the video camera 720 may beprovided on other parts of the head-mounted device 702. The video camera720 may be configured to capture images at various resolutions or atdifferent frame rates. Many video cameras with a small form-factor, suchas those used in cell phones or webcams, for example, may beincorporated into an example of the HMD 702.

Further, although FIG. 7A illustrates one video camera 720, more videocameras may be used, and each may be configured to capture the sameview, or to capture different views. For example, the video camera 720may be forward- or outward-facing to capture at least a portion of thereal-world view perceived by the user. This forward facing imagecaptured by the video camera 720 may then be used to generate anaugmented reality where computer generated images appear to interactwith the real-world view perceived by the user.

The sensor 722 is shown on the extending side-arm 716 of thehead-mounted device 702; however, the sensor 722 may be positioned onother parts of the head-mounted device 702. The sensor 722 may includeone or more of a gyroscope or an accelerometer, for example. Othersensing devices may be included within, or in addition to, the sensor722 or other sensing functions may be performed by the sensor 722.

The finger-operable touch pad 724 is shown on the extending side-arm 714of the head-mounted device 702. However, the finger-operable touch pad724 may be positioned on other parts of the head-mounted device 702.Also, more than one finger-operable touch pad may be present on thehead-mounted device 702. The finger-operable touch pad 724 may be usedby a user to input commands. The finger-operable touch pad 724 may senseat least one of a position and a movement of a finger via capacitivesensing, resistance sensing, or a surface acoustic wave process, amongother possibilities. The finger-operable touch pad 724 may be capable ofsensing finger movement in a direction parallel or planar to the padsurface, in a direction normal to the pad surface, or both, and may alsobe capable of sensing a level of pressure applied to the pad surface.The finger-operable touch pad 724 may be formed of one or moretranslucent or transparent insulating layers and one or more translucentor transparent conducting layers. Edges of the finger-operable touch pad724 may be formed to have a raised, indented, or roughened surface, soas to provide tactile feedback to a user when the user's finger reachesthe edge, or other area, of the finger-operable touch pad 724. If morethan one finger-operable touch pad is present, each finger-operabletouch pad may be operated independently, and may provide a differentfunction.

FIG. 7B illustrates an alternate view of the wearable computing deviceillustrated in FIG. 7A. As shown in FIG. 7B, the lens elements 710, 712may act as display elements. The head-mounted device 702 may include afirst projector 728 coupled to an inside surface of the extendingside-arm 716 and configured to project a display 730 onto an insidesurface of the lens element 712. Additionally or alternatively, a secondprojector 732 may be coupled to an inside surface of the extendingside-arm 714 and configured to project a display 734 onto an insidesurface of the lens element 710.

The lens elements 710, 712 may act as a combiner in a light projectionsystem and may include a coating that reflects the light projected ontothem from the projectors 728, 732. In some embodiments, a reflectivecoating may not be used (e.g., when the projectors 728, 732 are scanninglaser devices).

In alternative embodiments, other types of display elements may also beused. For example, the lens elements 710, 712 themselves may include: atransparent or semi-transparent matrix display, such as anelectroluminescent display or a liquid crystal display, one or morewaveguides for delivering an image to the user's eyes, or other opticalelements capable of delivering an in focus near-to-eye image to theuser. A corresponding display driver may be disposed within the frameelements 704, 706 for driving such a matrix display. Alternatively oradditionally, a laser or LED source and scanning system could be used todraw a raster display directly onto the retina of one or more of theuser's eyes. Other possibilities exist as well.

FIG. 8A illustrates another wearable computing system according to anexemplary embodiment, which takes the form of an HMD 802. The HMD 802may include frame elements and side-arms such as those described withrespect to FIGS. 7A and 7B. The HMD 802 may additionally include anon-board computing system 804 and a video camera 806, such as thosedescribed with respect to FIGS. 7A and 7B. The video camera 806 is shownmounted on a frame of the HMD 802. However, the video camera 806 may bemounted at other positions as well.

As shown in FIG. 8A, the HMD 802 may include a single display 808 whichmay be coupled to the device. The display 808 may be formed on one ofthe lens elements of the HMD 802, such as a lens element described withrespect to FIGS. 7A and 7B, and may be configured to overlaycomputer-generated graphics in the user's view of the physical world.The display 808 is shown to be provided in a center of a lens of the HMD802, however, the display 808 may be provided in other positions. Thedisplay 808 is controllable via the computing system 804 that is coupledto the display 808 via an optical waveguide 810.

FIG. 8B illustrates another wearable computing system according to anexemplary embodiment, which takes the form of an HMD 822. The HMD 822may include side-arms 823, a center frame support 824, and a bridgeportion with nosepiece 825. In the example shown in FIG. 8B, the centerframe support 824 connects the side-arms 823. The HMD 822 does notinclude lens-frames containing lens elements. The HMD 822 mayadditionally include an on-board computing system 826 and a video camera828, such as those described with respect to FIGS. 7A and 7B.

The HMD 822 may include a single lens element 830 that may be coupled toone of the side-arms 823 or the center frame support 824. The lenselement 830 may include a display such as the display described withreference to FIGS. 7A and 7B, and may be configured to overlaycomputer-generated graphics upon the user's view of the physical world.In one example, the single lens element 830 may be coupled to the innerside (i.e., the side exposed to a portion of a user's head when worn bythe user) of the extending side-arm 823. The single lens element 830 maybe positioned in front of or proximate to a user's eye when the HMD 822is worn by a user. For example, the single lens element 830 may bepositioned below the center frame support 824, as shown in FIG. 8B.

FIG. 9 illustrates a schematic drawing of a wearable computing deviceaccording to an exemplary embodiment. In system 900, a device 910communicates using a communication link 920 (e.g., a wired or wirelessconnection) to a remote device 930. The device 910 may be any type ofdevice that can receive data and display information corresponding to orassociated with the data. For example, the device 910 may be a heads-updisplay system, such as the head-mounted devices 102, 802, or 822described with reference to FIGS. 7A-9.

Thus, the device 910 may include a display system 912 comprising aprocessor 914 and a display 916. The display 910 may be, for example, anoptical see-through display, an optical see-around display, or a videosee-through display. The processor 914 may receive data from the remotedevice 930, and configure the data for display on the display 916. Theprocessor 914 may be any type of processor, such as a micro-processor ora digital signal processor, for example.

The device 910 may further include on-board data storage, such as memory918 coupled to the processor 914. The memory 918 may store software thatcan be accessed and executed by the processor 914, for example.

The remote device 930 may be any type of computing device or transmitterincluding a laptop computer, a mobile telephone, or tablet computingdevice, etc., that is configured to transmit data to the device 910. Theremote device 930 and the device 910 may contain hardware to enable thecommunication link 920, such as processors, transmitters, receivers,antennas, etc.

In FIG. 9, the communication link 920 is illustrated as a wirelessconnection; however, wired connections may also be used. For example,the communication link 920 may be a wired serial bus such as a universalserial bus or a parallel bus. A wired connection may be a proprietaryconnection as well. The communication link 920 may also be a wirelessconnection using, e.g., Bluetooth® radio technology, communicationprotocols described in IEEE 802.11 (including any IEEE 802.11revisions), Cellular technology (such as GSM, CDMA, UMTS, EV-DO, WiMAX,or LTE), or Zigbee® technology, among other possibilities. The remotedevice 930 may be accessible via the Internet and may include acomputing cluster associated with a particular web service (e.g.,social-networking, photo sharing, address book, etc.).

It should be understood that for situations in which the embodimentsdiscussed herein collect and/or use any personal information about usersor information that might relate to personal information of users, theusers may be provided with an opportunity to opt in/out of programs orfeatures that involve such personal information (e.g., information abouta user's preferences or a user's contributions to social contentproviders). In addition, certain data may be anonymized in one or moreways before it is stored or used, so that personally identifiableinformation is removed. For example, a user's identity may be anonymizedso that no personally identifiable information can be determined for theuser and so that any identified user preferences or user interactionsare generalized (for example, generalized based on user demographics)rather than associated with a particular user.

XI. Listing Suggestions for Gaze-Based Advertisement Marketplace

FIG. 10 is a flow chart illustrating a method according to an exemplaryembodiment. The method 1000 shown in FIG. 10 is described by way ofexample as being carried out by a server system in order to provideadvertisement-marketplace functionality. However, it should beunderstood that exemplary methods, such as method 1000, may be carriedout by other systems or combinations of systems, without departing fromthe scope of the invention.

Method 1000 involves the server system receiving a listing request thatindicates an offer to sell advertisement rights to an advertisementspace, where the listing request corresponds to a first user-account, asshown by block 1002. The server system also determines an advertisementvalue for the advertisement space, as shown by block 1004. Further, inresponse to the listing request, the server system updates anadvertising-space database with a listing for the advertisement space,where the listing is at a listing price that is based on the determinedadvertisement value, as shown by block 1006. The server system may alsomake the listing for the advertisement space available via anetwork-based advertisement marketplace, as shown by block 1008.

In an exemplary embodiment, the advertisement value that is determinedat block 1004 is based on detected occurrences of the advertisementspace in gaze data that is received from a plurality of wearablecomputing devices. Further, the gaze data from each wearable computingdevice is indicative of the respective wearer-view associated with thewearable computing device. For example, the gaze data from each wearablecomputing device may take the form of point-of-view video that iscaptured at the wearable computing device. As such, the gaze data uponwhich an advertisement value is based may include a number ofpoint-of-view videos (e.g., a respective point-of-view video from eachof the wearable computing devices).

Gaze data may additionally or alternatively take forms other thanpoint-of-view video. For example, the gaze data from a given wearablecomputing device may take the form of point-of-view images captured by aforward- or outward-facing camera on the wearable computing device. As aspecific example, a given wearable computing device may periodicallytake a picture, and then send the picture to the server system for usein generating wearer view data. To do so, the wearable computing devicemay analyze point-of-view video for one or more advertisement spaces,and generate a screen capture of the video when and advertisement spacedetected. The wearable computing device may then send the screen captureto the server system. Other examples are also possible.

Since the gaze data from a given wearable computing device is generallyindicative of the wearer-view of the wearable computing device's wearer,the gaze data is generally indicative of what the wearer of the deviceis actually looking at. Further, since the wearer-view data is based onthe gaze data, the wearer-view data is indicative of actual views of theadvertisement space by wearers. For instance, the wearer-view data mayprovide an indication of how many people are looking at a particularadvertisement space, which people are actually looking at a particularadvertisement space, when people are looking at a particularadvertisement space, and/or how long people are actually looking at aparticular advertisement space, among other information. As such, thewearer-view data may help to more accurately determine what anadvertising space is worth.

Since the gaze data from a given wearable computing device is generallyindicative of the wearer-view of the wearable computing device's wearer,the gaze data is generally indicative of what the wearer of the deviceis actually looking at. Further, since the wearer-view data is based onthe gaze data, the wearer-view data is indicative of actual views of theadvertisement space by wearers. For instance, the wearer-view data mayprovide an indication of how many people are looking at a particularadvertisement space, which people are actually looking at a particularadvertisement space, when people are looking at a particularadvertisement space, and/or how long people are actually looking at aparticular advertisement space, among other information. As such, thewearer-view data may help to more accurately determine what anadvertising space is worth. Methods and systems for determining anadvertisement value using gaze data will be described in greater detailwith reference to FIGS. 17A, 17B, and 18.

In some cases, the advertisement value for the advertisement space mayhave already been calculated when the listing request is received atblock 1002, using methods such as those described in reference to FIGS.17A, 17B, and 18. In this case, block 1004 may simply involve queryingan ad-value database to retrieve the predetermined advertisement value.

In other cases, the advertisement value may not yet have been calculatedwhen the listing request is received at block 1002. In such cases, block1004 may involve the server system responsively analyzing gaze data inorder to determine the advertisement value, using methods such as thosedescribed in reference to FIGS. 17A, 17B, and 18. Note that in some ofthese cases, analyzing the gaze data may take a considerable amount oftime (e.g., more time than a typical user will want to wait, andpossibly days, weeks, or even longer). As such, the server system mayindicate that time is needed to determine the advertisement value sothat the user can choose whether or not to wait for the advertisementvalue to be determined. If the user elects not to wait, then the serversystem may notify the user once the advertisement value is determined(e.g., via an indication sent to the user's user-account).

Further, in some cases, even when the advertisement value has beencalculated before receipt of the listing request at block 1002, theserver system may still analyze gaze data at block 1004 in order toupdate the predetermined advertisement value. This may be useful in theevent it has a significant amount of time since the advertisement valuewas determined (e.g., long enough that the advertisement value is likelyto have changed based on gaze data received in the interim). This may bethe default procedure, or may be triggered when a certain amount of timehas passed since the advertisement value was determined (e.g., longenough that the advertisement value is likely to have changed based ongaze data received in the interim). In the latter case, the amount oftime that triggers an update to the advertisement value may be setdepending upon the goals of the particular implementation.

XII. Exemplary Terms and Conditions of Sale in an AdvertisementMarketplace

In an exemplary method, such as method 1000, a listing request may takevarious forms and/or include various different types of information,depending upon the embodiment. For example, a listing request mayinclude data that: (a) identifies and/or defines the advertisement spaceto be listed, (b) identifies the user-account to be associated with thelisting, (c) data demonstrating that the user-account is authorized tolist the advertisement space, (d) data defining the terms and conditionsunder which the advertisement rights are being offered, and/or (e) othertypes of data related to the requested listing and/or the user account.

In a further aspect, an exemplary system may allow for certainconditions to be placed on a listing. Accordingly, an exemplary methodmay further involve receiving, in conjunction with the listing request,one or more condition indications which each indicate a condition of theadvertisement rights offered in the listing for the advertisement space.Such condition indications may be included in the listing request, orreceived separately from the listing request. Examples of conditionsthat may be specified may include: (a) a time period for which theadvertisement rights will be provided, (b) a portion of theadvertisement space to be provided, (c) one or more timeshareconditions, and (d) an advertisement-type restriction for theadvertisement space. Others types of conditions are also possible.

Furthermore, an exemplary system may allow a listing to be created for acertain type of contract or agreement. Functionally, the contract mayspecify a set of conditions that define the advertisement rights thatwill be provided when an advertiser purchases the advertisement rights.For example, a listing may specify a contract for:

-   -   (a) A sale transferring ownership of the advertisement space        from the first user-account to the second user-account.    -   (b) An exclusive lease of the advertisement space (e.g., an        agreement conveying all advertisement rights for the term of the        lease, such as a day, month, year, and so on).    -   (c) A partial exclusive lease of the advertisement space (e.g.,        an agreement conveying all advertisement rights for a portion or        portions of the term of the lease, such as between 4:00 pm and        6:00 pm every day during a month-long lease).    -   (d) A timeshare lease of the advertisement space (e.g., an        agreement to conveying the right to have an advertisement        displayed in a rotation with other advertisements, without any        specific time period being set aside for a given one of the        advertisements in the rotation).

Other types of contracts or agreements are also possible. Further, insome embodiments, an advertisement marketplace may provide pre-definedcontracts, which can be selected by a user for a listing. Additionallyor alternatively, an advertisement marketplace may allow a user tocreate a custom contract by specifying the terms and conditions desiredby the user. Further, in some embodiments, an advertisement marketplacemay allow a user to create a custom contract by modifying a pre-definedcontract. In a further aspect, an exemplary system may be configured tofacilitate a transaction for the advertisement rights to anadvertisement space that was listed using a method such as method 100.For example, an exemplary system may be configured to receive a purchaserequest from a second user-account, which indicates a desire to purchasea listing from a first user-account. In response to such a request, anexemplary system may facilitate a transaction for the advertisementrights to the advertisement space by, e.g., creating a contract for theadvertisement rights as specified in listing, between the firstuser-account and the second user-account.

XIII. Exemplary Pricing in an Advertisement Marketplace

In an exemplary advertisement marketplace, various types of pricing maybe implemented. In some embodiments, all advertisement spaces may belisted with the same pricing structure. In other embodiments, differentadvertisement spaces may be listed with different pricing structures.Further, in some embodiments, a single listing for an advertisementspace may offer multiple types of pricing structures, so that apurchaser of the advertisement space can select the pricing structurethat fits their needs.

Examples of possible pricing structures include: (a) various types offixed-rate pricing, (b) various types of variable-rate pricing, and/or(c) various types of auction-based pricing. It should be understood thatother pricing structures may additionally or alternatively beincorporated, without departing from the scope of the invention.

In a further aspect, in order to help provide more accurate pricing foradvertisement spaces, an exemplary system may extrapolate from theoccurrences of an ad-space that are detected in gaze data to estimatehow many views occurred in the viewing population as a whole. Theadvertisement value and/or the price based on the advertisement valuemay therefore take into account the estimated number of views by theviewing population. This may be useful as there may be many cases wherethose that view an advertisement space are not wearing a wearablecomputer that is configured to provide gaze data, and/or where awearable computer does not capture an advertisement that is viewed byits wearer. As one specific example, consider the case where it isassumed that one out of every thousand views will be captured in gazedata. In this case, if the value of ten occurrences that are detected ingaze data during a one-month period is determined to be $100, the serversystem may estimate are 10,000 views that are worth $10 each. As such,the advertisement value based on all views may be determined to be$100,000 per month. Other examples are also possible.

Note that in some instances, the extrapolation from the occurrences ingaze data may assume that the users who provide gaze data are generallyrepresentative of the viewing population as a whole. However, it is alsopossible that the users who provide gaze data (e.g., those who ownwearable computers and opt in to a program to share gaze data) may notbe representative of the viewing population as a whole. In this case, anexemplary system may be configured to account for such differences whenextrapolating from the gaze data. The particular techniques used todetermine a total number of ad-space views based on the occurrencesdetected in gaze data may vary from application to application,depending on the characteristics of the viewing population, thedemographics of those who provide gaze data, and/or other factors.

In a further aspect, demographic biases created by differences betweenwearable-computing-device wearers and the population as a whole (or acertain subset of the population) may also be accounted for whencalculating individual ad-value contributions upon which theadvertisement value is ultimately based. For example, consider a town oranother geographic region where an “over-55” age group is known to makeup 60% of the viewing population as a whole, but only makes up 12% ofthe wearable-computing-device users in that area (as indicated byregistered wearers of the given age group and/or observed occurrences ofan ad space in gaze data from wearers in the age group). Accordingly,when determining the value for an advertisement space in this town, thedata from the 12% of wearers who are in the over-55 age group may begiven a 60% weight, while the data from the other 88% of wearers fromwhich gaze data is received may be given a 40% weight. Many otherexamples are also possible.

In yet another aspect, users who are selling advertisement rights totheir ad spaces may be provided options to control and/or affect whattypes of advertisements are displayed in their ad space and/or the termsunder which certain advertisers can advertise in their advertisementspace. For example, a certain user might not want a certain type ofadvertisement in their advertisement space for moral or ethical reasons,or for any other reason, and might specify that there advertisementspace cannot be used for this type of advertisement, or might indicatethat the listing price and amount charge should be higher for this typeof advertisement. For example, a user who does not like the idea of anadvertisement for an alcoholic beverage in their advertisement space,may specify that such an advertisement should cost three times thestandard rate (e.g., the relative advertisement value) for their adspace. As another example, a user who is a fan of a particularelectronics company may want an advertisement from the company in theirad space, and therefore may specify that this company should be chargedhalf of the standard rate for their advertisement space. Many otherexamples are also possible.

A. Fixed-Rate Pricing

As noted, fixed-rate pricing may be used for advertisement spaces in anexemplary advertisement marketplace. For example, in some instances, theserver system may simply set the listing price for advertisement rightsat the gaze-data based advertisement value. According, when a methodsuch as that described in reference to FIGS. 17A, 17B, and 18 is used todetermine the advertisement value for an advertisement space, performingthe method may effectively determine the listing price at which theadvertisement space can be listed.

In some cases, the price at which an advertisement space can be listedmay differ from the determined advertisement value. However, the listingprice may still be set to a fixed value or fixed rate that is based onthe advertisement value for the advertisement space. In particular, therate for a fixed-price listing may be adjusted according to the termsand conditions specified by the listing. For example, if anadvertisement value is determined in terms of dollars per month, and alisting request specifies that the advertisement space can be purchasedon a day-to-day basis, the system may calculate a daily rate that isbased at least on part on the monthly advertisement value.

As another example, an advertisement value may be determined with theassumption of exclusive use for a certain term. In this case, if thelisting specifies that the advertisement space can be purchased on atimeshare basis (e.g., placing an advertisement in a rotation with otheradvertisements), the rate at which the timeshare rights to theadvertisement space can be listed may be derived from the advertisementvalue for exclusive use. For example, the advertisement value may bedivided by the number of shares to determine the rate for one share ofthe timeshare. For instance, consider an advertisement space for whichexclusive use is valued at $30.00 per month. If the owner wishes tooffer this advertisement space as a timeshare to be split between tenadvertisers, each share may be listed at $3.00 per month. Othertechniques for determining a timeshare price based on an advertisementvalue that assumes exclusive use are also possible.

More generally, many different techniques may be implemented to accountfor variations from the advertisement value due to terms and conditionsof a listing. Furthermore, the manner in which the advertisement valueis used to determine the price for a listing may vary based on otherfactors and/or for other reasons, without departing from the scope ofthe invention.

B. Variable-Rate Pricing

As noted, variable-rate pricing may be used for advertisement spaces inan exemplary advertisement marketplace. In particular, the listing pricemay be a variable rate that is based on the advertisement value for theadvertisement space.

For example, at an initial time of the listing, the variable rate may beset equal to or based on the advertisement value. The server system maysubsequently update the variable rate based at least in part on gazedata that is received after the initial time of the listing.

FIG. 11A is a flow chart illustrating a method for updating a variablerate for advertisement rights, according to an exemplary embodiment. Themethod 1100 shown in FIG. 11A is described by way of example as beingcarried out by a server system to provide the described functionality.However, it should be understood that method 1100 may be carried out byother systems or combinations of systems, without departing from thescope of the invention.

Method 1100 may be carried out by a server system after the initial timewhen a listing for advertisement rights to an advertisement space wasmade available. More specifically, at some point after the initiallisting, the server system may receive additional gaze data from aplurality of wearable computing devices, as shown by block 1102. Theserver system may analyze the additional gaze data to detect occurrencesof the advertisement space in the additional gaze data, as shown byblock 1104. The server may then update the variable rate based at leastin part on occurrences of the advertisement space in the additional gazedata, as shown by block 1106.

The server may use various techniques to update the variable rate atblock 1106. In some embodiments, the server may use the occurrences ofthe advertisement space in the additional gaze data to update theadvertisement value for the advertisement space. The server can in turnuse the updated advertisement value as a basis for updating the variablerate. For instance, taking into account the additional gaze data, theserver may implement a method such as that described in FIGS. 17A, 17B,and 18 to update the advertisement value for the advertisement space.

FIG. 11B is a flow chart illustrating another method for updating avariable rate for advertisement rights, according to an exemplaryembodiment. The method 1150 shown in FIG. 11B is described by way ofexample as being carried out by a server system to provide the describedfunctionality. However, it should be understood that method 1150 may becarried out by other systems or combinations of systems, withoutdeparting from the scope of the invention.

Method 1150 may be implemented in a scenario where the advertisementvalue upon which the listing price is initially based, is based onwearer-view data. Further, the wearer-view data upon which theadvertisement value is based, is in turn based on the detectedoccurrences of the of the advertisement space in gaze data. In addition,like method 1100 of FIG. 11A, method 1150 of FIG. 11B may be carried outby a server system after a listing for advertisement rights to anadvertisement space is made available.

More specifically, after a listing has been made available in theadvertisement marketplace, the server system may receive additional gazedata, as shown by block 1102. The server may then analyze the additionalgaze data to detect additional occurrences of the advertisement space inthe gaze data, as shown by block 1104. As such, the server may updatethe wearer-view data for the advertisement space based on the additionaloccurrences that are detected in the additional gaze data, as shown byblock 1106. The server may then use the updated wearer-view data as abasis for updating the variable rate, as shown by block 1108.

In an exemplary embodiment, at block 1108, the server may use theupdated wearer-view data to update the advertisement value, and in turnuse the updated advertisement value to update the variable rate. Forinstance, taking into account the additional gaze data, the server mayimplement a method such as that described in FIGS. 17A, 17B, and 18 toupdate the wearer-view data and in turn the advertisement value for theadvertisement space.

In a further aspect of an exemplary method, such as method 1100 or 1150,the updated advertisement value may be based solely on the additionalgaze data, or may take into account some or all of previously-receivedgaze data as well as the additional gaze data. As such, updating theadvertisement value may involve re-determining the advertisement valueusing only data received between the current and previous instance inwhich the advertisement value was determined. By using this approach,the server is effectively “starting from scratch” each time it updatesthe advertisement value, as none of the gaze data that was previouslyused to determine the advertisement value, will be used to whenre-determining the advertisement value. Alternatively, the server maycombine the additional gaze data with some or all of the previouslyreceived data, and use this combination of gaze data to update theadvertisement value.

In some implementations, an advertiser may be charged for anadvertisement space on a per-occurrence basis. In such an embodiment,the server may search gaze data to detect any occurrences of anadvertisement space, and determine an ad-value contribution of eachdetected occurrence. The server may then use the ad-value contributionsfor detected occurrences of the advertisement space to determine theamount to be charged for the user-account.

For example, consider an implementation where an advertiser pays foradvertisement rights on a per-occurrence basis, and is billed on apredefined billing period or cycle (e.g., weekly or monthly). In thisscenario, the server may determine the amount owed for a given billingperiod by summing the ad-value contributions of the ad-space occurrencesthat are detected during the billing period. Other billing techniquesmay also be used to charge advertisers on a per-occurrence basis.

When an advertiser is billed for advertisement rights on aper-occurrence basis, the advertiser may be charged a fixed amount foreach occurrence, or may be charged an amount that can vary fromoccurrence to occurrence.

More specifically, in some cases, it may be assumed that all views of anadvertisement are of equal value (or that views of differing values mayrevert to a fixed average or median value over time). In this and othercases, the same amount may be charged per occurrence.

However, an exemplary method may also account for the fact that someviews of an advertisement may be more valuable to an advertiser thanother views. For example, the amount due according of a given occurrencemay vary depending on factors such as the duration of occurrence, howfocused the viewer was on the advertisement space (e.g., as indicated bya focus value), and/or characteristics of the particular user who viewedthe ad, among others. Accordingly, an exemplary method may involvedetermining ad-value contributions for individual occurrences of anadvertisement space, and determining an amount to be paid in a billingcycle based on the individual ad-value contributions during the billingcycle. Methods for determining ad-value contributions of individualoccurrences of an advertisement space are described in greater detailwith reference to FIGS. 9B and 10.

C. Auctions

As noted, pricing for an advertisement space may also be set using anauction process. For example, FIG. 12 is a flow chart illustrating anauction process for advertisement rights, according to an exemplaryembodiment.

More specifically, method 1200 may involve the server system receivingan auction-listing request to offer advertisement rights to anadvertisement space via an auction, where the auction-listing requestcorresponds to a first user-account, as shown by block 1202. The servermay also determine an advertisement value for the advertisement space,where the advertisement value is based on detected occurrences of theadvertisement space in gaze data, as shown by block 1204. Further, inresponse to the auction-listing request, the server system may add anauction listing for the advertisement space to an advertising-spacedatabase, where the auction listing indicates that the advertisementrights are available for purchase via an auction process that is basedat least in part on the determined advertisement value, as shown byblock 1206. The server may then make the auction listing available via anetwork-based advertisement marketplace, as shown by block 1208.

The auction process may be based on the determined advertisement valuein various ways. For example, a minimum bid may be set to a certainpercentage of the advertisement value. For instance, the auction processmay require the first bid to be at least 60% of the advertisement value.Similarly, a reserve price for the auction may also be set to apercentage of the advertisement value. For instance, in the aboveexample where the minimum bid is 60% of the advertisement value, thereserve may be set at 80% percent of the advertisement value. Therefore,while bidding may start at 60% of the advertisement value, the seller isnot obligated to sell until bidding reaches at least 80% of theadvertisement value. Many other examples are also possible.

XIV. Valuation Requests

As noted an exemplary advertisement marketplace may supportvaluation-request functionality, via which a user can request theadvertisement value and/or the potential listing price for anadvertisement space. For example, FIG. 13 is a flow chart illustrating amethod for providing an advertisement space valuation in anadvertisement marketplace, according to an exemplary embodiment.

More specifically, method 1300 involves the server system receiving avaluation request that indicates an advertisement space for valuation,where the request is associated with a certain user-account (e.g.,received from a computing device that is associated with theuser-account), as shown by block 1302. The system then determines theadvertisement value for the advertisement space, which is based ondetected occurrences of the advertisement space in gaze data, as shownby block 1304. Then, based at least in part on the determinedadvertisement value, the system may determine a listing price for theadvertisement space, as shown by block 1306. The system can then respondto the valuation request by sending an indication of the determinedlisting price, as shown by block 1308.

The valuation request may indicate the advertisement space by way ofdata that may itself be used to search the gaze data for theadvertisement space and/or by way of data from which search criteria maybe derived. The server may accordingly use the data that identifies theadvertisement space to search for occurrences of the advertisement spacein subsequently-received gaze data and/or in past gaze data that hasbeen stored. In particular, the server may use a method such as thatdescribed in FIGS. 17A, 17B, and 18 to determine the advertisement valuebased on such gaze data. The server may then send an indication of theadvertisement value to the user that requested the valuation.Additionally or alternatively, the server may send an indication of apotential listing price if the user were to list the advertisement space

In some cases, when a valuation request is received at block 1302, thesystem may already have the data needed to determine the advertisementvalue, or in some instances, may even have the advertisement valueavailable. In particular, the system may have already have generatedwearer-view data based on previously-detected occurrences of theidentified advertisement space in gaze data, and further, may havedetermined an advertisement value based on this wearer-view data. Thismay occur in a number of scenarios. For example, a user may have made ageneral request to search gaze data for advertisement spaces that can belisted via their user account, provided general authorization to searchfor such advertisement spaces, or previously requested that the systemcreate wearer-view data specifically for the identified advertisementspace. Other examples are also possible.

Therefore, in order to determine the advertisement value for theindicated advertisement space at block 1304, the system may first querythe advertisement-space database to determine if an entry for theadvertisement space exists and if so, whether the entry indicatesexisting wearer-view data and/or an advertisement value. If anadvertisement value is indicated, the system may simply retrieve theadvertisement value. If wearer-view data exists, but no advertisementvalue is indicated or the advertisement value is determined to beout-of-date (e.g., not based on the latest available wearer-view data),then the system may use the existing wearer-view data to determine theadvertisement value.

On the other hand, if no ad-value is available and no wearer-view dataexists (or if existing wearer-view data is determined to beout-of-date), then the system may need to search gaze data for theadvertisement space in order to generate the wearer-view data upon whichthe advertisement value can be based. To do so, the server may searchstored gaze data for occurrences of the advertisement space and/or maysearch future gaze data for occurrences of the advertisement space. Morespecifically, in an exemplary embodiment, the server may carry out amethod such as those described in FIGS. 17A, 17B, and 18 in order todetermine to the advertisement value for the indicated advertisementspace.

In a further aspect, note that if a listing request is received foradvertisement space that is not yet include in the ad-space database,such as in method 100 of FIG. 1, then the system may automatically treatthis listing as a valuation request and/or prompt the user as to whetherthey would like valuation to be performed. Once the advertisement valuehas been determined, the system may then allow the user to proceed withlisting the advertisement space.

XV. Identifying Potential Advertisement Spaces

A. Automated Suggestion of Potential Advertisement Spaces

In some embodiments, an exemplary system may be configured to searchgaze data for physical spaces that are not valued and/or that are notlisted for sale in the advertisement marketplace. When such potentialadvertisement spaces are detected, an exemplary system may identify auser that is authorized to list the physical space for sale as anadvertisement space, and/or may determine a potential listing price forthe advertisement space. Further, the system may then notify theauthorized user that the physical space can be listed as advertisementspace in the advertisement marketplace.

For example, FIG. 14 is flow chart illustrating a method for locatingpotential advertisement spaces, according to an exemplary embodiment. Inparticular, method 1400 involves the server system searching gaze datafor any occurrence of an advertisement space, as shown by block 1402.Upon detecting one or more occurrences of a given advertisement space inthe gaze data, the server may determine whether or not the givenadvertisement space is an unlisted advertisement space, as shown byblock 1404. If it is determined that the advertisement space isunlisted, then the system may determine a user-account that isassociated with the given advertisement space, as shown by block 1406.In addition, the system may determine an advertisement value for theadvertisement space that is based on the detected occurrences of theadvertisement space, as shown by block 1408. The system may then send anindication of an ad-space suggestion message to the user-account, whichindicates a listing price for at least the advertisement space that isbased on the determined advertisement value, as shown by block 1410.

B. Request for Identification of Advertisement Spaces that can be Listed

In an exemplary advertisement marketplace, method 1400 may beimplemented to automatically identify potential advertisement spacesthat are currently unlisted, and notify users that are authorized tolist these potential advertisement spaces of this possibility. Thisfeature may be automatic, and thus may be performed without targetingpotential advertisement spaces for any particular user. However, in someembodiments, features may be provided to allow a user to specificallyrequest a search for unlisted advertisement spaces that can by listed bythe user. This feature may help a user to provide advertisement spacesthat they may have otherwise been unaware of, and may be helpful inother scenarios as well.

For example, FIG. 15 is flow chart illustrating a method for providing asearch-request feature, according to an exemplary embodiment. Morespecifically, method 1500 involves the server system receiving a searchrequest associated with a first user-account, as shown in block 1502. Inresponse to the search request, the server system searches gaze data todetect any occurrences of any advertisement space associated with thefirst user-account, as shown by block 1504. In response to detecting oneor more associated advertisement spaces in the gaze data, the server maysend the first user-account a search-result message that indicates theassociated advertisement spaces that were located in the search, asshown by block 1506.

At block 1506, the server may wait for a certain time period and/or waituntil a certain number of associated advertisement spaces are detected,before sending the search-result message. Accordingly, the search-resultmessage may indicate a number of potential advertisement spaces for theuser-account. Alternatively, the server may send a separatesearch-result message upon detection of each associated advertisementspace.

In a further aspect of an exemplary embodiment, the search and/or thesearch results may be limited to unlisted advertisement spaces.Accordingly, if unlisted advertisement spaces are detected at block1504, the server may provide the requesting user-account with anindication of the unlisted advertisement spaces at block 1506.

In another aspect, the search-result message may also include a listingprice for some or all of the associated advertisement spaces. As such,method 1500 may further involve the system determining an advertisementvalue for at least one associated advertisement space, usinggaze-data-based methods such as those described herein. The listingprice may then be determined based on the advertisement value, andincluded in the search-result message. Provided with the listing pricefor an associated advertisement space, the user may send a listingrequest for the advertisement space. When the server receives such alisting request, the server may create a listing using an exemplarymethod, such as method 100.

C. Advertisement-Interest Indication Functionality

In another aspect, an exemplary advertisement marketplace may includefeatures that allow a user (i.e., an “advertiser”) that is interested inan unlisted advertisement space to indicate their interest. For example,the advertisement marketplace may build a database of unlistedadvertisement space using techniques similar to those described inblocks 1402 to 1406 of Figure, or using other techniques. Theadvertisement marketplace may then provide an interface via whichadvertisers can browse and/or search the unlisted advertisement spaces.This interface may allow an advertiser to indicate their interest inpurchasing an unlisted advertisement space and/or to request that theuser who is authorized to list the advertisement space be notified ofthis interest, in hopes that the user will then chose to list theadvertisement space in the advertisement marketplace.

In other cases, an exemplary advertisement marketplace may allow anadvertiser to indicate a interest in purchasing advertisement rights toa physical space that the system has not yet identified as anadvertisement space. When the system receives such a request, the systemmay first need to determine which user is authorized to list theadvertisement space (if any), so that an indication of the advertiser'sinterest can be sent to the user.

FIG. 15 is flow chart illustrating a method that providesadvertiser-request functionality, according to an exemplary embodiment.In particular, method 1500 involves the server system receiving anadvertisement-interest indication associated with a first user-account,where the advertisement-interest indication indicates an advertisementspace, as shown by block 1502. The system then determines anadvertisement value for the advertisement space, which is based ondetected occurrences of the advertisement space in gaze data, as shownby block 1504. The system then identifies a second user-account that isassociated with the advertisement space, as shown by block 1506. Thesystem then sends an advertisement-interest notification, whichindicates a listing price for the advertisement space that is based onthe determined advertisement value, to the second user-account, as shownby block 1508.

In an exemplary embodiment, the associated user-account identified atblock 1506 is a user-account of the user or one of multiple users thatare authorized to list the advertisement space (e.g., the owner of theadvertisement space or a representative of the owner). Further, in someimplementations, multiple user-accounts may be identified at block 1506(e.g., both the user-account of the owner and a user-accounts for arepresentative of the owner).

XVI. Detecting Advertisement Spaces in Gaze Data

As noted above, various embodiments involve analysis of gaze data todetect and/or identify when advertisement spaces occur in gaze data.Referring back to FIG. 2, in order to detect ad-space occurrences, anexemplary server system 200 may employ various types of video and/orimage-processing techniques. For instance, advertisement server system204 may implement various well known and yet-to-be-developed techniquesfor object recognition in video and/or still images in the process ofrecognizing advertising spaces.

In some cases, an advertisement space may be identified in gaze data byway of the advertisement that is displayed in the advertisement space.For example, ad-space database 210 may include data related to whichspecific advertisements are being displayed in which advertisementspaces (and may further indicate when there is no advertisement beingdisplayed in a given advertisement space). As such, ad-valuation server212 may search for advertisements that are currently being displayed ingaze data. To do so, the ad-valuation server may user various visualsearch techniques that are now known or yet to be developed in order toidentify an advertisement in gaze data.

In other cases, an advertisement space may itself be identified, withoutnecessarily relying on the particular advertisement that is beingdisplayed in the advertisement space. (Note that this functionality maybe particularly useful in cases where an advertisement space is empty.)In such an embodiment, detecting that an advertisement space occurs ingaze data may involve recognizing when the gaze data includes an objector a certain combination of objects that are associated with aparticular advertisement space. For example, to recognize advertisementspace on the bumper of a particular car, gaze data may be analyzed foran object shaped and/or having coloration that is characteristic of acar bumper. Further, the gaze data may be analyzed for an object havingsuch a shape and/or coloration in conjunction with a license platehaving a certain license plate number. In such an embodiment, the serversystem may consider an occurrence of a bumper in combination with thelicense plate number for the specific car to be an occurrence of theadvertisement space on the car's bumper. Many other examples are alsopossible.

In some cases, searching gaze data from a large number of wearablecomputing devices for a large number advertisement spaces may be dataintensive. Accordingly, an exemplary server and or wearable computingdevices may implement pre-processing techniques to tag and ID certaintypes of objects or certain types of information in gaze data, which mayhelp to speed up the process of detecting advertisement spaces. In someinstances, wearable computing devices and/or the server may also storegaze data for processing when, e.g., a wearable computing device isoffline, or when the amount of real-time data being collected isgenerally less. For example, a server may use certain processingresources to receive incoming gaze data during the day, when more gazedata may be received, and then re-assign these processing resources toanalyze stored gaze data for advertisement spaces at night, when lessnew gaze data may be received.

In some embodiments, a server system may utilize location data to detectan occurrence of an advertisement space in gaze data. For example, aserver system may determine or be provided with the geographic locationof a particular advertisement space (e.g., the GPS coordinates of theadvertisement space). Then, when the advertisement space is detected ingaze data from a particular wearable computing device, the server maydetermine the location of the wearable computing device. If thiswearable computing device is located such that the advertisement spacecould be visible to the wearer of the wearable computing device (e.g.,within a predetermined distance from the location of the advertisementspace), then the server system may consider this an occurrence of theadvertisement space. However, if the wearable computing device thatprovided the gaze data is located such that the advertisement spacecould not be viewed by the wearer (e.g., not within a predetermineddistance from the location of the advertisement space), then the serversystem may not consider this an occurrence of the advertisement space.

As another example, a server system may use the geographic location of aparticular advertisement space to limit the gaze data that is monitoredfor the advertisement space. For instance, the server may determine thelocations of wearable computing devices from which gaze data isreceived. As such, the server may only monitor gaze data that isreceived from wearable computing devices that are located within apredetermined distance from the advertisement space. Other methods thatutilize the location of an advertisement space when detectingoccurrences of the advertisement space in gaze data are also possible.

In some embodiments, radio frequency identification (RFID) may be usedto help detect occurrences of an advertisement space in gaze data. Inparticular, an advertisement space may be associated with a certain RFIDtag, and wearable computing devices may be configured with RFID readers.As such, when a wearable computing device detects an RFID tag from anadvertisement space, the wearable computing device may relay this to theserver system. For instance, when the wearable computing device detectsan RFID that is associated with an advertisement space, it may insertmetadata into the gaze data which indicates the RFID tag and the time atwhich the RFID tag was detected. Alternatively, the wearable computingdevice may send a separate message indicating that the RFID tag wasdetected at a particular time. In either case, the server system canthen search for the associated advertisement space in gaze data that isreceived from the wearable computing device at or near the time when theRFID tag is detected. This may help the server system to moreefficiently detect occurrences of advertisement spaces, as the timingwith which the RFID tags are detected may indicate, for example, timesin corresponding point-of-view video where the advertisement space islikely to occur. Further, various types of RFID may be utilized, such asnear-field communications (NFC) and/or other types of RFID, dependingupon the implementation.

In some embodiments, barcodes may be used to help detect occurrences ofan advertisement space in gaze data. For instance, a barcode thatidentifies an advertisement space may be displayed within or near to anadvertisement space. The server system may then search for barcodeswithin gaze data. When a barcode associated with a particularadvertisement space is detected, the server may consider this to be anoccurrence of the advertisement space, or may treat this as a factorthat, along with other factors, can indicate that there is an occurrenceof the advertisement space in the gaze data. Various types of barcodes,such as high capacity color barcodes (HCCBs) and/or quick response (QR)codes may be utilized in such an embodiment. Other types of barcodes arepossible as well.

In a further aspect, machine-readable codes may also be associated witha particular ad that is displayed in an ad space. For example, a certainadvertisement may be associated with a certain QR code. As such, theadvertisement may be detected when the QR code is detected in gaze data.

Further, in some embodiments, machine-readable codes may be associatedwith a certain combination of a particular advertisement and aparticular user-account. As such, each copy of the same advertisementmay have a different QR code, which uniquely identifies the user-accountthat is authorized to sell the advertisement space. As a result,detecting a QR code in gaze data may enable an exemplary server systemto positively identify not only the advertisement that is displayed inthe advertisement space, but also which copy of the advertisement hasbeen detected. This may be useful in a scenario where the advertisementspace itself is difficult to detect, and in other scenarios as well.

It should be understood that the above techniques for detectingoccurrences of advertisement spaces are not intended to be limiting.Other techniques are also possible.

XVII. Determining the Value of an Advertisement Space

As noted, in some embodiments, an exemplary system may be configured touse gaze data to determine advertisement values for advertisementspaces. Further, an exemplary system may be configured to determine anadvertisement value for many types of physical spaces; many of which maynot have been valued using traditional advertisement valuationtechniques.

FIG. 17A is a flow chart illustrating a method according to an exemplaryembodiment. This method may be implemented by a computing device, and inparticular, by a server system, in order to value an advertisement spacebased on point-of-view gaze data received from a number of wearablecomputing devices (which may be referred to interchangeably as wearablecomputing devices). Note that wearable computing devices may also bereferred to as wearable computers herein. Further, a server system thatimplements an exemplary method may be referred to as an ad-valuationsystem, as an ad-valuation server, or simply as a server.

As shown by block 1702, method 1700 involves a server system receivinggaze data from a number of wearable computing devices. The server systemanalyzes the gaze data from the wearable computing devices to detectoccurrences of an advertisement space in the gaze data, as shown byblock 1704. The server system then generates wearer-view data for theadvertisement space, which is based on detected occurrences of theadvertisement space in the gaze data, as shown by block 1706. Thewearer-view data can then be used as a basis for determining anadvertisement value for the advertisement space, as shown by block 1708.Once the advertisement value is determined, the server system may causea computing system to make the advertisement space available forpurchase at the determined advertisement value, as shown by block 1710.

In an exemplary method 1700, the gaze data is received from a number ofwearable computing devices. Further, the gaze data from each wearablecomputing device is generally indicative of a respective wearer-viewassociated with the given wearable computing device. For example, thegaze data from each wearable computing device may take the form ofpoint-of-view video that is captured at the wearable computing device.As such, the gaze data that is analyzed by the server system may includea number of point-of-view videos (e.g., a respective point-of-view videofrom each of the wearable computing devices).

The gaze data from some or all of the wearable computing devices thatprovide gaze data may additionally or alternatively take forms otherthan point-of-view video. For example, the gaze data from some or all ofthe wearable computing devices may take the form of respectivepoint-of-view images captured by a forward- or outward-facing camera onthe respective wearable computing device. As a specific example, a givenwearable computing device may periodically take a picture, and then sendthe picture to the server system for use in generating wearer view data.To do so, the wearable computing device may analyze point-of-view videofor one or more advertisement spaces, and generate a screen capture ofthe video when and advertisement space detected. The wearable computingdevice may then send the screen capture to the server system. Otherexamples are also possible.

Since the gaze data from a given wearable computing device is generallyindicative of the wearer-view of the wearable computing device's wearer,the gaze data is generally indicative of what the wearer of the deviceis actually looking at. Further, since the wearer-view data is based onthe gaze data, the wearer-view data is indicative of actual views of theadvertisement space by wearers. For instance, the wearer-view data mayprovide an indication of how many people are looking at a particularadvertisement space, which people are actually looking at a particularadvertisement space, when people are looking at a particularadvertisement space, and/or how long people are actually looking at aparticular advertisement space, among other information. As such, thewearer-view data may help to more accurately determine what anadvertising space is worth.

As noted above, when occurrences of an advertisement space are detectedin gaze data, an exemplary method 1700 may involve generatingwearer-view data that is based on the detected occurrences. As such, anexemplary server system 204 may be configured to carry out an exemplarymethod 1700 or portions thereof for many different advertisement spaces.Generally, the accuracy of the ad-space valuation will typicallyincrease as the number of wearable computing devices providing gaze dataincreases. However, the gaze data may be collected from any number ofwearable computing devices without departing from the scope of theinvention.

To facilitate determining an advertisement value for a givenadvertisement space, the wearer-view data may provide various types ofinformation. For example, the wearer-view data for a given advertisementspace may include, for each detected occurrence of the givenadvertisement space: (a) data indicating the particular wearablecomputing device that provided the gaze data in which the advertisementspace occurred, (b) data indicating a user-profile associated with theparticular wearable computing device, (c) data indicating a time of thedetected occurrence, (d) a duration of the detected occurrence, and/or(e) other information.

Generally, the function of generating wearer-view data for theadvertisement space, as shown in block 1706 of method 1700, may varydepending upon the information to be included in the wearer-view data.In an exemplary embodiment, detecting an occurrence of an advertisingspace in the gaze data may serve as a trigger for the server system togenerate wearer-view data recording the fact that the occurrence wasdetected. Further, to generate the wearer-view data for a givenoccurrence, the server system may extract information from the gaze datain which the occurrence was detected. The extracted information (orinformation derived from the extracted information) may be included inthe wearer-view data generated for the detected occurrence.

A. Per-Occurrence Data for an Advertisement Space

In some embodiments, the server system 204 may update the wearer-viewdatabase 1108 upon each detected occurrence of an advertisement space.For example, the server system may generate a record in the wearer-viewdatabase for each detected occurrence of an advertisement space. In suchan embodiment, the record for a given occurrence of an advertisementspace may include: (a) an indication of the particular wearablecomputing device that provided the gaze data in which the advertisementspace occurred, (b) an indication of a user-profile associated with theparticular wearable computing device, (c) a time of the occurrence,and/or (d) a duration of the occurrence.

The wearer-view data for a given occurrence of an advertisement spacemay indicate the corresponding wearable computing device that providedthe gaze data in which the advertisement space occurred. In such anembodiment, the server system may determine the corresponding wearablecomputing device in various ways. For instance, consider an embodimentwhere the server system receives point-of-view (POV) video stream from anumber of wearable computing devices. In such an embodiment, the serversystem may establish a communication session to receive the video streamfrom a given one of the wearable computing devices, and as part ofestablishing and/or participating in the session, may receive anidentifier of the wearable computing device. (Note that variousprotocols, which are well known in the art, may be used to receive a POVvideo stream and/or to receive other forms of gaze data.) Additionallyor alternatively, metadata in the gaze data itself may include anidentifier of the wearable computing device that is providing the gazedata. Other techniques for determining which wearable computing devicecorresponds to a particular occurrence of an advertisement space arealso possible.

As further noted above, the wearer-view data for a given occurrence ofan advertisement space may indicate an associated user-profile, which isassociated with the wearable computing device that provided the gazedata having the particular occurrence. The server system may determinethe associated user-profile in various ways. For example, the server maydetermine the identifier for the corresponding wearable computing devicein a manner such as described above or otherwise. The server may thenlook up a user-profile of a user that is registered to use or isotherwise associated with the corresponding wearable computing device(e.g., by querying a user database that indicates which users areassociated with which wearable computing devices). Alternatively, auser-identifier may be provided in the course of receiving the gaze data(e.g., in a communication session or in metadata). In such anembodiment, the server system may use the user-identifier to access auser-profile for the user. As another alternative, the user-profileitself may be received directly from the device (e.g., during thecommunication session in which the gaze data is received, as metadataincluded in the gaze data, or in a separate message that is associatedwith the gaze data). Other techniques for determining a correspondinguser-profile for a particular occurrence of an advertisement space arealso possible.

In a further aspect, when the wearer-view data for a given occurrenceindicates the associated user-profile, the wearer-view data may simplyinclude an identifier of the associated user-profile. In such anembodiment, the data from such user-profiles may be stored in one ormore separate user-profile databases. In this case, the server may usethe identifiers of the associated user-profiles to retrieve the datafrom the actual user-profiles. Alternatively, some or all of the datafrom the associated user-profile may be included in the wearer-view datafor the advertisement space (e.g., in wearer-view database 1108).

In a further aspect, the server system may include a time stamp in thewearer-view data that is generated for a given occurrence. The timestampmay indicate the time at which the occurrence of the advertisement spacewas detected. Additionally or alternatively, the timestamp may indicatea time that is derived from time data included in the gaze data. Forexample, point-of-view video from a given wearable computing device mayinclude time data indicating when the video was recorded by the wearablecomputing device. As such, the server system may use this time data togenerate a timestamp for an occurrence that is detected in suchpoint-of-view video. For instance, the server system may determine aframe or frames of the video that include the advertisement space, anduse a time stamp or time stamps of the frame or frames to generate thetimestamp for the detected occurrence. Other techniques for generating atimestamp for a particular occurrence of an advertisement space are alsopossible.

In another aspect, the wearer-view data for a given occurrence of anadvertisement space may indicate the duration of the given occurrence.Accordingly, the server system may be configured to determine theduration of a given occurrence of an advertisement space. For instance,in the above example where POV video includes time data, the serversystem may use timestamps on frames of the video to determine theduration of time the first frame of the video that includes theadvertisement space and the last subsequent and consecutive frame thatincludes the advertisement space. Alternatively, the server system mayimplement its own timer to determine the duration of a given occurrenceof an advertisement space. Other techniques for determining the durationof a particular occurrence of an advertisement space are also possible.

In a further aspect, when generating wearer-view data for a givenoccurrence, the server may consider whether the wearable computingdevice that corresponds to a given occurrence was being worn during theoccurrence. In particular, if the corresponding wearable computingdevice is not being worn at the time of the detected occurrence, theserver may adjust or change the wearer-view data that is generated inresponse to detecting the occurrence. For example, when the wearablecomputing device is not being worn, the server may interpret this tomean that the gaze data from the wearable computing device is unlikelyto represent what the wearer is actually viewing. Accordingly, theserver may include an indication that the wearable computing device wasnot being worn in the wearer-view data that is created for such anoccurrence. Further, server may adjust the wearer-view data so as todecrease the weight of such an occurrence when determining theadvertisement value for the advertisement space, or may ignore theoccurrence entirely (e.g., by refraining from generating any wearer-viewdata for the occurrence).

B. Summary Data for an Advertisement Space

In some embodiments, the wearer-view data for a given advertisementspace may include summary data for the advertisement space such as: (a)a list of which wearable computing devices viewed the advertisementspace (e.g., which wearable computing devices provided gaze data inwhich one or more occurrences were detected), (b) a list of theuser-accounts or the user-profiles that are associated with the wearablecomputing devices that have viewed the advertisement space, (c) a totalview count indicating the total number of detected occurrences of theadvertisement space, (d) a total view duration of the advertisementspace, (e) an average view duration for occurrences of the advertisementspace, and/or (f) a view rate that indicates how frequently theadvertisement space occurs in the gaze data (e.g., occurrences/hour,occurrences/month, etc.). The wearer-view data for a given advertisementspace may additionally or alternatively include other types of summarydata for the advertisement space.

In order to keep the above and other such summary data substantiallycurrent, the server system may update the wearer-view data for anadvertisement space each time the advertisement space is detected ingaze data. For example, when the server system detects an advertisementspace in gaze data from a given wearable computing device, the serversystem may update the wearer-view data by: (a) adding the given wearablecomputing device to a list of wearable computing devices that haveviewed the advertisement space (if the wearable computing device is noton the list already), (b) adding the user-account or the user-profilethat is associated with the given wearable computing device to a list ofuser-accounts or user-profiles that have viewed the advertisement space,(c) incrementing the total view count for the advertisement space, (d)determining the duration of the occurrence and adding the determinedduration to a total view duration for the advertisement space, and/or(e) determining the duration of the occurrence and adding the determinedduration and recalculating the average view duration to account for thedetermined duration. Other examples are possible as well.

In some embodiments, the wearer-view data for each advertisement spacemay include only summary data such as that described above, and thus maynot include per-occurrence data for each detected occurrence of anadvertisement space. However, it is also possible that the wearer-viewdata for a given advertisement space may include only per-occurrencedata, or may include both per-occurrence data and summary data for theadvertisement space.

C. Focus Data for an Occurrence

In some embodiments, the wearer-view data for a given advertisementspace may include focus data, which is generally indicative of theamount of attention paid to an advertisement space by viewers of theadvertisement space. The focus data may help to provide a more accuratevaluation for the advertisement space by helping take into account thefact that not all views are necessarily equal, since the amount ofattention paid to the advertisement space may vary between views. Insuch an embodiment, the server system may determine a focus value for adetected occurrence (as described above) when it generates wearer-viewdata for the occurrence, or may determine a focus value at a later time.

D. Use of Summary Data for Advertisement Valuation

As noted above, an exemplary method 1700 may involve using thewearer-view data for an advertisement space to determine anadvertisement value for the advertisement space. Various types ofwearer-view data may be utilized when determining an advertisementvalue. For instance, various types of the summary data described aboveand/or various types of the per-occurrence data described above may beused to determine the advertisement value for a given advertisementspace. An exemplary valuation method may also incorporate other types ofdata in addition to wearer-view data. Further, the manner in which agiven type of wearer-view data is used to determine an advertisementvalue may vary depending upon the implementation.

In some embodiments, the advertisement value for a given advertisementspace may be based on summary data for the advertisement space. Forexample, the advertisement value may be based at least in part on thetotal view count for an advertisement space (e.g., the total number ofoccurrences that are detected in the gaze data). In such an embodiment,the total number of occurrences may be tracked over all time.Alternatively, the total number of occurrences may be tracked over apredetermined period of time (e.g., a year, a month, a week, or acustom-defined time period). In an exemplary embodiment thatincorporates total view count, the determined advertisement value willtypically increase as the total number of occurrences increases.Further, the manner in which the total view count is used to determineadvertisement value may vary, depending upon the implementation.

As another example, the advertisement value for a given advertisementspace may be based at least in part on a view rate for the advertisementspace (e.g., the rate at which occurrences of the advertisement spaceare detected in the gaze data). For instance, the wearer-view data mayindicate a number of views per month, per week, per day, per hour, etc.In such an embodiment, the rate may be based on detected occurrencesover all time. Alternatively, the rate may be based on occurrencesduring a predetermined period of time (e.g., during a year, a month, aweek, or a custom-defined time period). In an exemplary embodiment thatincorporates view rate, the determined advertisement value willtypically increase as the view rate increases. Further, the manner inwhich the view rate is used to determine advertisement value may vary,depending upon the implementation.

In the above examples, the advertisement value is determined based onsummary data that generally does not differentiate one detectedoccurrence from another. However, some embodiments may apply furtherintelligence to account for the fact that some views of an advertisementspace may be more valuable to an advertiser than others.

For example, the advertisement value for a given advertisement space maybe based at least in part on a total view duration and/or an averageview duration for the advertisement space. In such an embodiment, thetotal view duration and/or the average view duration may be calculatedfrom all detected occurrences of the advertisement space or from arepresentative sample of occurrences. In either case, the total viewduration and/or the average view duration may be calculated over alltime, or may be calculated over a predetermined period of time (e.g., ayear, a month, a week, or a custom-defined time period). In an exemplaryembodiment that incorporates total view duration and/or the average viewduration, the determined advertisement value will typically increase asthe total view duration and/or the average view duration increases.Accordingly, views that last longer will generally contribute more tothe advertisement value and/or be weighted more heavily when determiningthe advertisement value. It should be understood that the manner inwhich the total view duration and/or the average view duration is usedto determine advertisement value may vary, depending upon theimplementation.

As another example, the server may determine focus values for all or arepresentative sample of the detected occurrences of an advertisementspace. The server may then average the focus values for the detectedoccurrences to determine an average focus value for the advertisementspace. The server can then use the average focus value to determine theadvertisement value for the advertisement space.

In a further aspect, an exemplary embodiment may help account for thefact that views of an advertisement space by certain people may beconsidered more valuable than views of the same advertisement space byother people. More specifically, in an exemplary embodiment, wearers mayopt-in to a program or otherwise give permission for information fromtheir user-profile to be used to value advertisement spaces. Varioustypes of information from an associated user-profile may then be used todetermine how valuable a given occurrence of an advertisement space is.For instance, a user-profile for a wearer may include: (a) consumerinformation such as spending habits, locations of purchases, amounts ofpurchases, types or categories of purchases, timing of purchases, etc.,(b) demographic information such as age or age group, ethnicity,nationality, sex, location of residence, and/or location of workplace,(c) contact and/or social networking information such as a wearer'scontacts, and possibly data indicating a purchasing influence of thewearer with regard to their contacts (e.g., data indicating anycorrelation of the wearer's purchasing history to the wearers' friends'purchasing histories), and/or (d) other information such as income, jobor job type, other job details, hobbies, interests, and so on.

Therefore, since the occurrence of an advertisement space in gaze datafrom a given wearable computing device may be interpreted to mean thatthe wearer of the given wearable computing device has viewed or isviewing the advertisement space, information from user-profiles thatwearer-view data associates with a given advertisement space may provideinformation about the type or types of people that an advertisementspace reaches and/or of the characteristics of people that theadvertisement space reaches. As a result, this information may be usedto more accurately determine what types of people are viewing theadvertisement space, and value the advertisement space accordingly. Inparticular, an exemplary server may place greater weight on occurrenceof an advertisement space associated with certain people and/or certaintypes of people when determining the advertisement value for a givenadvertisement space.

For example, the server may determine a respective income level for theuser-profile associated with each occurrence. The server may thenaverage the determined income levels to calculate an average incomelevel for viewers of the advertisement space, and use the average incomelevel as input data to determine the advertisement value for theadvertisement space. Alternatively, the server may determine an incomerange of the determined income levels, and use the income range as aninput to the ad-value calculation for the advertisement space. Otherexamples are also possible.

It should be understood that the advertisement value for a givenadvertisement space may be based upon one type of summary data or acombination of various types of summary data. For example, in oneimplementation, the total number of views, the view rate, the averageview duration, and one or more characteristics of the associateduser-profiles, could all be used as inputs when calculatingadvertisement value. Many other examples are also possible.

E. Use of Per-Occurrence Ad-Value Contributions for AdvertisementValuation

In some embodiments, a server system may determine an advertisementvalue for an advertisement space by first determining an individualadvertisement-value contribution for each detected occurrence of theadvertisement space. The advertisement-value contribution for a givenoccurrence may be based on information from the user-profile associatedwith the occurrence and/or on other information related to theoccurrence. The collective knowledge provided by all the individualadvertisement-value contributions may then be used to determine theadvertisement value for the advertisement space and/or be used todetermine summary data for the advertisement space, which may in turn beused to determine the advertisement value.

FIG. 17B is a flow chart illustrating a method for determiningadvertisement value, according to an exemplary embodiment. Inparticular, FIG. 17B illustrates a method 1750 in which theadvertisement value for an advertisement space is based on individualad-value contributions of occurrences of the advertisement space in gazedata.

More specifically, method 1750 involves monitoring gaze data from anumber of wearable computing devices for occurrences of a particularadvertisement space, as shown by block 1752. Each time an occurrence ofthe advertisement space is detected, as shown by block 1754, the serversystem may further determine an advertising-value contribution of theoccurrence, as shown by block 1756. The server system may then determineadvertising-value contributions for a number of occurrences by repeatingblocks 1754 and 1756 for a number of detected occurrences of theadvertisement space. The server system may then use theadvertising-value contributions for the detected occurrences of theadvertisement space as a basis for determining the advertisement valuefor the advertisement space, as shown by block 1758.

In some embodiments, such as method 1750 of FIG. 17B, the ad-valuecontribution for each occurrence of an advertisement space may bedetermined upon detecting the occurrence in the gaze data. However, itshould be understood that the ad-value contribution for some or alloccurrences of an advertisement space may be calculated at a later time,based on wearer-view data that is stored as the occurrences are detectedin the gaze data.

A server may use various techniques to determine the individual ad-valuecontribution for a given occurrence of an advertisement space in gazedata. Such techniques may utilize various different factors to determinethe ad-value contribution for a given occurrence. In some embodiments,the server may use a weighting value for an occurrence to determine thead-value contribution of the occurrence. In particular, the server maydetermine a weighting value that generally indicates the particularoccurrence's value relative to a “standard” occurrence of theadvertisement space. The weighting value for the particular occurrencemay then be applied to a base advertising-value contribution todetermine the advertising-value contribution for the particularoccurrence. In such an embodiment, the weighting value may be based onvarious factors or combinations of factors, such as the particularwearable computing device from which the gaze data including theparticular occurrence was received, the duration of the occurrence,characteristics of the person who viewed (e.g., as indicated by theuser-profile associated with the occurrence), the focus value of theoccurrence, and/or other factors.

As a specific example, the ad-value contribution for each occurrence ofthe advertisement space may be a dollar amount that is attributed to theoccurrence. As such, the server may determine a dollar amount for theadvertisement value by summing the ad-value contributions. As anotherexample, the ad-value contribution for each occurrence of theadvertisement space may be a price rate (e.g., dollars per month,dollars per view, etc.) that is attributed to a respective occurrence ofthe advertisement space. As such, the server may determine theadvertisement value by summing the ad-value contributions to get anoverall price rate. Other examples are also possible.

Once a server system has determined the individual ad-valuecontributions for a number of occurrences of the particularadvertisement space, the server may use various techniques to determinethe advertisement value for the advertisement space. For example, insome embodiments, the server may determine an average advertising-valuecontribution by averaging the advertising-value contributions of some orall of the detected occurrences. The server may then use the averageadvertising-value contribution as a basis for determining theadvertisement value for the advertisement space. As a specific example,the server may determine an ad-value contribution for each occurrence inthe same manner as the overall price or rate for the advertisementspace, but using the assumption that all occurrences of theadvertisement space are identical to the occurrence. The server may thendetermine a dollar amount or price rate for the advertisement space byaveraging the ad-value contributions determined in this manner. Otherexamples are also possible.

It should be understood that techniques described herein for determiningan advertisement value based on the ad-value contributions are notintended to be limiting. Other techniques for determining anadvertisement value based on the ad-value contributions of individualoccurrences are also possible, without departing from the scope of theinvention.

F. Valuation of an Advertisement Space on a Per-Advertisement Basis

Some embodiments may involve determining a value an advertisement spacethat is specific to a particular type of advertisement. For example, anadvertisement server may determine a value for an advertisement spacewhen the advertisement space is used to display an advertisement for aparticular type of product (e.g., for clothing or for a movie).

Further, in some embodiments, an exemplary method may be implemented forad-specific valuation of an advertisement space based on the extent towhich the advertisement space reaches the target market of theadvertisement. For instance, wearer-view data may be used to determinewho is viewing an advertisement space. The advertisement space valuationmay then be based on the correlation between those who view theadvertisement space and the target market of the specific advertisement.

In such an embodiment, an exemplary method may utilize wearer-view dataindicating user-profiles associated with occurrences of an advertisementspace in the gaze data. As such, the server may analyze the associateduser-profiles to determine one or more characteristics of those who haveviewed the advertisement space. More specifically, an exemplary methodmay involve determining a group of user-profiles associated with theadvertisement space (e.g., user-profiles that are associated withwearable computing devices that captured the advertisement space intheir respective gaze data). Then, based on characteristics of theassociated user-profiles, the server may determine one or more viewercharacteristics of the group. The viewer characteristics of the groupmay be interpreted as indicative of a “standard” viewer of theadvertisement space. As such, the viewer characteristics of the groupmay incorporate when determining the advertisement value for a specifictype advertisement.

For example, some embodiments may involve determining both: (a) theviewer characteristics of the group of associated user-profiles and (b)one or more target-viewer characteristics for the particularadvertisement. The server may then compare the viewer characteristics ofthe group to the target-viewer characteristics and, based on thecomparison, determine the advertisement value for the particularadvertisement in the advertisement space.

In some embodiments, the advertisement value for the particularadvertisement may be further based on the location of the advertisementspace. In particular, there may be a relationship between thecharacteristics of a particular advertisement and the location ofadvertisement space, and an exemplary method may help to account forsuch a relationship. In such cases, a weighting factor may be applied toincrease or decrease the advertisement value depending upon therelationship between the location of the advertisement space and thecharacteristics of the advertisement.

For example, consider an advertisement for a clothing product and anadvertisement space that is located near to a shopping area and/or nearto a store where the clothing product can be purchased. Thisadvertisement space may generally be considered more valuable when usedto display the advertisement for the clothing product than when used todisplay an advertisement for a type of product that cannot be purchasednearby. Accordingly, a weighting factor may be applied to increase theadvertisement value for the clothing product in the advertisement space.Similarly, the weighting factor may function to decrease theadvertisement value for a product that cannot be purchased nearby.

As another example, consider an advertisement for a movie and anadvertisement space that is located near to a movie theater that isshowing the movie. This advertisement space may generally be consideredmore valuable when used to display the advertisement for the movie thanwhen used to display an advertisement for a movie that is not in anynearby theaters. Accordingly, a weighting factor may be applied toincrease the advertisement value of the advertisement space for a moviethat is showing in the nearby theater. Similarly, the weighting factormay decrease the advertisement value for the movie that is not in anynearby theaters. Other examples are also possible.

In a further aspect, some implementations of methods 1700 and 1750 mayutilize the type of advertisement as the characteristic of theadvertisement upon which the advertisement value is based. In such anembodiment, all advertisements of the same type may be evaluated in thesame way. As such, the advertisement value in such an embodiment may ineffect be determined for the type of advertisement in the advertisementspace (rather than specifically for an individual advertisement).Alternatively, the type of advertisement may be one of a number offactors, such that an advertisement space may be valued differently fordifferent advertisements that are classified as being the same type ofadvertisement.

In yet another aspect, the advertisement value may vary for a particularadvertisement, if it is determined that the advertisement is more orless likely to interest wearers who view it. In particular, someembodiments may increase the advertisement value for an advertisement ina given advertisement space (and/or the amount that an advertiser isultimately charged, if this differs from the advertisement value), whenthe advertisement that is more likely to interest the people whotypically view the particular advertisement space. However, in otherembodiments, the opposite approach is also possible. In particular, anexemplary system may actually decrease the advertisement value for anadvertisement in a given advertisement space (and/or the amount that anadvertiser is ultimately charged, if this differs from the advertisementvalue) when the advertisement that is more likely to interest the peoplewho typically view the particular advertisement space.

While this may seem counter-intuitive in one sense, this strategy ofcharging less for more valuable advertisement selection may providelong-term gains. In particular, by displaying advertisements that arelikely to interest users, the advertising format as a whole gainscredibility with the viewing public, which may result in more peopleviewing the advertisement space in the long-term. Put another way, ifpeople are disinterested in what they see in an advertisement space,they may choose not to view and/or ignore what is displayed in theadvertisement space in the future. Therefore, by reducing the amountcharged for advertising that interests users, advertisers areincentivized to provide advertising that is interesting. Providing suchincentivizes may in turn increase the chances for long-term success inthe advertising space and similar types of advertising spaces, thuscreating more long term value for the seller and/or a third-party thatis brokering advertisement sales.

FIG. 18 is a flow chart illustrating a method for using multiple factorsto determine the ad-value contribution of a given ad-space occurrence ingaze data, according to an exemplary embodiment. In method 1800 of FIG.18, a pre-determined base contribution for an occurrence of theadvertisement space is weighted according to multiple factors in orderto determine an ad-value contribution for each occurrence of anadvertisement space in gaze data for a given user-account. Theadvertisement value for a given advertisement space may then bedetermined from the collective knowledge provided by the ad-valuecontributions of the all or a representative subset of its occurrencesin gaze data. While method 1800 is described by way of example as beingimplemented by a server system, this by another device or system, or bya combination of the server system and/or other devices and systems.

Method 1800 involves the server system determining a weighting value(weighting_value_(i)) to be applied for an occurrence of anadvertisement space (O_(i)) in gaze data. The weighting value is basedon m different factors (f_1 _(i) to f_m_(i)). In particular, the serversystem may determine a value for a first factor (f_1 _(i)) with respectto a given occurrence of an advertisement space in, as shown by block1802. The server system may then determine a benchmark value for thefirst factor (bm_f_1 _(i)), as shown by block 1804. This benchmark maybe the average or a mean value for the given factor over a sample set ofdifferent occurrences, or may be a predefined standard value for thegiven factor. The benchmark value may be determined in other ways aswell.

In any case, the server system may determine a relationship between thedetermined value of the first factor for the given occurrence and thebenchmark value with respect to the first factor (e.g., f_1 _(i)/bm_f_1_(i)), as shown by block 1806. If there are additional factors toconsider (e.g., if m is greater than one), as indicated by block 1808,then the server may repeat blocks 1802 to 1806 for the additionalfactors, until the relationship between the given user-account andrelationship between the value for the given occurrence and the averagevalue has been determined for each factor. Once all the factors havebeen evaluated, the server system may use the determined relationshipsas a basis for determining a weighting value for the occurrence, asshown by block 1810.

Thus, for a given occurrence O_(i), and values for a set of n factorsf_1 to f_n, method 1800 may be implemented to determine aweighting_value_(i) as a function of the respective relationshipsbetween the values of factors for the respective occurrence f_1 _(i) tof_m_(i) and the respective benchmark value bm_f_1 _(i) to bm_f_m_(i).For example, weighting_value_(i) may be calculated as:weighting_value_(i) =F[(f_1_(i) /bm_f_1_(i)),(f_2_(i) /bm_f_2_(i)), . .. (f_m _(i) /bm_f_m _(i))]Note that the particular function of the relationships used may varyfrom implementation to implementation, depending upon the design goals.

Once the server has determined the weighting_value_(i) for a givenoccurrence O_(i) of an advertisement space, the server may use a basecontribution for an occurrence of the advertisement space detected inoccurrence O_(i) to calculate the ad-value contribution for theoccurrence as:ad_value_contribution_(i)=base_contribution_(i)*weighting_value_(i)

Further, the server system may repeat the above process to determine anad-value contribution for each of n occurrences of the advertisementspace in the gaze data. As such, the advertisement value for theadvertisement space may be determined as a function ofad_value_contribution_(i) for i equal 1 to n. For example, theadvertisement value for the advertisement space may be calculated bysumming ad_value_contribution₁ to ad_value_contribution_(n). Otherexamples are also possible.

It should be understood that many other types of information provided byand/or related to a given user-account may be considered, alone or incombination, when determining the ad-value contribution of anadvertisement space in gaze data for the given user-account. Further,information provided by and/or related to a given user-account may beconsidered in combination with other factors, such as duration of anoccurrence and/or a focus value associated with the occurrence, whendetermining the ad-value contribution for an occurrence.

G. Adjusting the Advertisement Value Based on Other Factors

In some embodiments, advertisement valuation may be based on other typesof data, in addition to wearer-view data. In such an embodiment, theadvertisement server may determine a base value for the advertisement,or a weighting to be applied to the advertisement value based on anintrinsic value of the advertisement space, which accounts for thecharacteristics of the advertisement space itself, and then adjust theintrinsic value according to the wearer-view data.

For example, in some embodiments, an exemplary method may use thegeographic location of the advertisement space as a further basis fordetermining the advertisement value for the advertisement space. Forexample, an advertisement that is located in a shopping area may have agreater intrinsic value than one that is located in an alley.Accordingly, an advertisement value that is determined based onwearer-view data may be adjusted based on the location of theadvertisement space. Other examples are also possible. Generally, thetype and/or the amount of adjustment that is applied due to the locationof an advertisement space may vary depending upon the particularimplementation.

Further, in some embodiments, the server may consider the type ofadvertisement space and/or the format in which advertisements can bedisplayed in the advertisement space when determining the advertisementvalue for the advertisement space. For example, an LCD billboard mightgenerally be considered more valuable than an equivalent printbillboard. As such, when an advertisement value is determined for abillboard based on wearer-view data, the determined advertisement valuemay be adjusted based on whether the billboard is an LCD or a printbillboard. Other examples are also possible. Generally, the type and/orthe amount of adjustment that is applied based on the type ofadvertisement space may vary depending upon the particularimplementation. Further, other adjustments, based on othercharacteristics of an advertisement space, are also possible.

In another aspect, an advertisement space may be blank (e.g., notdisplaying an advertisement) during some or all of the period in whichgaze data is being collected for purposes of determining theadvertisement value. The fact that an advertisement space is blank, asopposed to displaying an advertisement, may affect the gaze data for theadvertisement space because a blank space might attract less attentionfrom nearby people. Further, different advertisements may attractdifferent amounts of attention. Therefore, when an advertisement isdisplayed while gaze data is being collected, the particularadvertisement may itself affect the gaze data. As such, it is possiblethat wearer-view data for an advertisement space may be effected bywhether or not an advertisement space is blank, and if something isdisplayed in the advertisement space, what specifically is displayed.

Accordingly, an exemplary method may further involve determining apre-sale weighting factor for an advertisement space, which is based on:(a) whether the advertisement space is blank while gaze data is beingcollected and/or (b) the characteristics of what is displayed in theadvertisement space while gaze data is being collected. A server maythen use the pre-sale weighting factor for the advertisement space as afurther basis for determining the advertisement value for theadvertisement space.

As a specific example, an exemplary method may further the serverdetermining whether or not the advertisement space had an advertisementin place while receiving and analyzing the gaze data. Then, if theadvertisement space had an advertisement in place, the server may applya first adjustment to the wearer-view data before using the wearer-viewdata to determine the advertisement value (e.g., an adjustment orweighting factor that corresponds to the particular advertisement thatis displayed). On the other hand, if an advertisement was not displayedin the advertisement space, then the server may apply a secondadjustment to the wearer-view data (e.g., an adjustment that accountsfor the fact that no advertisement was displayed).

In such an embodiment, the server may determine whether or not theadvertisement space had an advertisement in place in various ways. Forexample, the server may query an advertisement space database todetermine whether the advertisement space is in use and if so, whatadvertisement is being displayed. Additionally or alternatively, theserver may analyze the gaze data itself (e.g., by analyzingpoint-of-view video in which the advertisement space is detected). Otherexamples are also possible.

In yet another aspect, some embodiments may implement gaze-datarequirements that require a certain amount of gaze data be analyzedbefore an advertisement space can be offered for sale in anad-marketplace system. For instance, an ad-marketplace system mayrequire that gaze data from a threshold number of devices have beenanalyzed before the determined advertisement value is deemed accurateenough to offer the advertisement space for sale via the marketplace.Additionally or alternatively, ad-marketplace system may require thatgaze data be monitored for a certain period of time (e.g., at least aweek) before the determined advertisement value is deemed accurateenough to offer the advertisement space for sale.

Further, in some embodiments, wearer-view data requirements may requirethat a certain amount of wearer-view data be generated before anadvertisement space can be offered for sale in an ad-marketplace system.For example, an ad-marketplace system may require that a certain numberof occurrences of an advertisement space be detected before thedetermined advertisement value is deemed accurate enough to offer theadvertisement space for sale via the marketplace (or in other words,require that the wearer-view data takes into account at least athreshold number of occurrences). Other examples are also possible.

XVIII. Use of Supplemental Gaze Data from Non-Wearable Computing Devices

In some embodiments, an exemplary method may incorporate supplementalgaze data from one or more non-wearable computing devices. In such anembodiment, the supplemental gaze data may include media captured by thenon-wearable computing devices. For example, supplemental gaze data maybe received from mobile phones, tablet computers, network-enabled videoand/or still cameras, and/or other non-wearable computing devices.

Similar to the gaze data from wearable computing devices, thesupplemental gaze data is generally indicative of a respective user-viewassociated with a device that provides supplemental gaze data.Accordingly, an exemplary method may further involve receivingsupplemental gaze data from one or more non-wearable computing devicesthat are registered to a given user-account. A server may then detectadditional occurrences of advertisement spaces in the supplemental gazedata, and factor the additional occurrences in when determining theadvertisement value for a given advertisement space.

However, because supplemental gaze data is captured at non-wearabledevices, supplemental gaze data may less reliably represent what theuser actually sees, as compared to gaze data captured by a wearabledevice that is physically worn on the user's person. Accordingly, in anexemplary method, the server may weight supplemental occurrences thatare detected in the supplemental gaze data in order to account for theincreased probability that the supplemental gaze data does not representwhat the user actually sees. For example, the server may weight asupplemental occurrence by a significance factor corresponding to thelikelihood that the corresponding supplemental gaze data is indicativeof a respective user-view associated with the non-wearable computingdevice that provided the supplemental gaze data in which thesupplemental occurrence was detected.

In a further aspect, systems may be implemented to actively searchsupplemental gaze data that is pre-recorded, such as image librariesthat are accessible via a network. For example, a server or anassociated system may be configured to analyze images from one or moreonline image albums to determine supplemental user-view data for theadvertisement space. In such an embodiment, the supplemental user-viewdata is based on occurrences of the advertisement space in the pluralityof images.

For example, the system may search image albums on a photo-sharingwebsite, social-network website, or another network source, foroccurrences of the advertisement space in the images. When an occurrenceis found, the system may generate supplemental user-view data for theoccurrence. For instance, many such web sites require that users open auser-account in order to create photo albums and/or share photos.Accordingly, the system may store data linking the occurrence of theadvertisement space to the user-account via which the image was shared.

In a further aspect, one image of an advertisement space may beindicative of a more valuable view of the advertisement space thananother image. As such, each image that includes the advertisement spacemay be evaluated for indications of how significant the occurrence is,so that the occurrence may be weighted accordingly when determining theadvertisement value.

For example, an exemplary method may involve analyzing one or moreimages from one or more image albums to detect any occurrences of theadvertisement space in the images. Then, for each image where anadvertisement space is detected, the system may determine an ad-valuecontribution and/or an advertising-value contribution for the givenimage (note that in some instances, the advertising-value contributionmay be used as the ad-value contribution). As a specific example,determining a prominence value corresponding to a prominence of theadvertisement space in the given image (e.g., a size and/or location ofthe advertisement space in the image), and then use the prominence valueas a basis for determining an ad-value contribution and/or anadvertising-value contribution for the given image. The system may thenuse any ad-value contributions from these images, such as any prominencevalues that are determined for any of the images, when determining theadvertisement value for a given advertisement space. Similarly, thesystem may use any advertising-value contributions from these images,such as any prominence values that are determined for any of the images,when determining the advertisement value for the advertisement space.

In a further aspect, data from GPS systems and other sensors, such asmagnetometers, accelerometers, and/or gyroscopes may providesupplemental gaze data. In some embodiments, GPS on a wearable computingdevice or another device (e.g., a mobile phone or tablet) may provide anindication of location, and a magnetometer on the same device mayprovide an indication of orientation, such that it may be inferred thata user of the device is in a certain location and is facing a certaindirection. Further, the location of an advertisement space may also bedetermined as described herein. Thus, if the user is inferred to befacing a location where and space is located, this may be considered aview of the advertisement space, and thus may be factored into thewearer-view data. Other examples of using GPS and/or sensor data toinfer supplemental gaze data are also possible.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method comprising:detecting, by a first computing device, one or more occurrences of afirst real-world surface in image data received from one or morewearable devices, wherein the image data is indicative of respectivewearer-views associated with the one or more wearable devices;determining, by the first computing device, that the first real-worldsurface is not listed in a listing database for augmented-realitydisplay rights to real-world surfaces; and responsive to determiningthat the first real-world surface is not listed in the listing database,the first computing device: determining a user-account associated withthe first real-world surface; based at least in part on the one or moredetected occurrences of the first real-world surface in the image data,determining an augmented-reality display value corresponding to thefirst real-world surface; and initiating a transmission of an electronicmessage to a second computing device corresponding to the user-accountthat is associated with the first real-world surface, wherein theelectronic message indicates the determined display value correspondingto the first real-world surface.
 2. The method of claim 1, wherein theelectronic message comprises a listing-suggestion message indicating onopportunity to list the first real-world surface in the listingdatabase.
 3. The method of claim 1, wherein determining that the firstreal-world surface is not listed in the listing database for displayrights to real-world surfaces comprises: determining thataugmented-reality overlay rights for the first real-world surface arenot listed in the listing database for augmented-reality overlay rightsto real-world surfaces.
 4. The method of claim 1, wherein the image datacomprises point-of-view (POV) image data received from the one orwearable devices, and wherein determining the display valuecorresponding to the first real-world surface comprises: using the POVimage data to generate wearer-view data for a real-world object; andusing the wearer-view data for the real-world object as a basis fordetermining a value corresponding to overlay of augmented-realitycontent on the first real-world surface.
 5. The method of claim 1,further comprising, receiving, by the computing system, alisting-suggestion request associated with the user account, wherein thelisting-suggestion request indicates an instruction to opt in to aservice that provides suggestions of real-world surfaces usable foroverlay of augmented-reality content.
 6. The method of claim 5, whereinthe detecting of the one or more occurrences of the first real-worldsurface in image data is responsive to receipt of the listing-suggestionrequest.
 7. The method of claim 1, further comprising receiving, by thecomputing system, a listing-suggestion request associated with the useraccount, wherein the listing-suggestion request indicates an instructionto opt in to a service that provides suggestions of real-world surfacesusable for overlay of augmented-reality content, wherein at least thedetection of the one or more occurrences of the first real-world surfacein the image data, and the determination that the first real-worldsurface is not listed in the listing database are conditioned upon thereceipt of the listing-suggestion request associated with the useraccount.
 8. The method of claim 1, wherein the first real-world surfaceis a non-display surface of a laptop computer.
 9. The method of claim 1,wherein the first real-world surface is located on an article ofclothing.
 10. The method of claim 1, wherein the first real-worldsurface is located on an automobile.
 11. The method of claim 1, whereindetermining the display value corresponding to the first real-worldsurface comprises: determining a value corresponding to overlay ofaugmented-reality content on the first real-world surface.
 12. A systemcomprising: at least one communication interface operable to communicatewith one or more computing devices associated with user-accounts; anon-transitory computer-readable medium; and program instructions storedon the non-transitory computer-readable medium and executable by atleast one processor to: (a) detect one or more occurrences of a firstreal-world surface in image data received from one or more wearabledevices, wherein the image data is indicative of respective wearer-viewsassociated with the one or more wearable devices; (b) determine that thefirst real-world surface is not listed in a listing database foraugmented-reality display rights to real-world surfaces; (c) responsiveto determining that the first real-world surface is not listed in thelisting database for display rights to real-world surfaces: (i)determine a user-account associated with the first real-world surface,(ii) based at least in part on the one or more detected occurrences ofthe first real-world surface in the image data, determine a displayvalue corresponding to the first real-world surface; and (iii) initiatea transmission of an electronic message to a second computing devicecorresponding to the user-account that is associated with the firstreal-world surface, wherein the electronic message indicates thedetermined display value for augmented-reality overlay rights to thefirst real-world surface.
 13. The system of claim 12, whereindetermining the display value corresponding to the first real-worldsurface comprises: determining a value corresponding to overlay ofaugmented-reality content on the first real-world surface.
 14. Thesystem of claim 12, further comprising receiving, by the computingsystem, a listing-suggestion request associated with the user account,wherein the listing-suggestion request indicates an instruction to optin to a service that provides suggestions of real-world surfaces usablefor overlay of augmented-reality content, wherein at least thedetermination that the first real-world surface is not listed in thelisting database for display rights to real-world surfaces isconditioned upon the receipt of the listing-suggestion requestassociated with the user account.
 15. The system of claim 12, whereinthe electronic message comprises a listing-suggestion message indicatingon opportunity to list the first real-world surface in the listingdatabase.
 16. A computer-implemented method comprising: receiving, by afirst computing system, respective point-of-view (POV) image data from aplurality of wearable computing devices; receiving, by the firstcomputing system, an indication of interest that identifies a firstreal-world surface, wherein the first real-world surface is associatedwith a first account and the indication of interest is associated with asecond account; and determining, by the first computing system, that thefirst real-world surface is not listed in a listing database foraugmented-reality display rights to real-world surfaces, andresponsively: (a) analyzing the POV image data from the plurality ofwearable computing devices to detect one or more occurrences of thefirst real-world surface in the POV image data; (b) when one or moreoccurrences of the first real-world surface are detected in the POVimage data, determining an augmented-reality display value correspondingto the first real-world surface, wherein the display value is determinedbased at least in part on the one or more detected occurrences of thefirst real-world surface; and (c) initiate a transmission of anelectronic message to a computing device corresponding to the firstuser-account, wherein the electronic message indicates the display valuecorresponding to the first real-world surface.
 17. The method of claim16, wherein the first real-world surface is a non-display surface of alaptop computer, part of an article of clothing, or part of anautomobile.
 18. The method of claim 16, wherein determining the displayvalue corresponding to the first real-world surface comprises:determining a value corresponding to overlay of augmented-realitycontent on the first real-world surface.
 19. The method of claim 16,further comprising receiving, by the computing system, alisting-suggestion request associated with the user account, wherein thelisting-suggestion request indicates an instruction to opt in to aservice that provides suggestions of real-world surfaces usable foroverlay of augmented-reality content, wherein at least the determinationthat the first real-world surface is not listed in the listing databasefor display rights to real-world surfaces is conditioned upon thereceipt of the listing-suggestion request associated with the useraccount.
 20. The method of claim 16, wherein the electronic messagecomprises a listing-suggestion message indicating on opportunity to listthe first real-world surface in the listing database.